{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "classify ants and bees\n",
    "https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quoting these notes,\n",
    "\n",
    "    In practice, very few people train an entire Convolutional Network from scratch (with random initialization), because it is relatively rare to have a dataset of sufficient size. Instead, it is common to pretrain a ConvNet on a very large dataset (e.g. ImageNet, which contains 1.2 million images with 1000 categories), and then use the ConvNet either as an initialization or a fixed feature extractor for the task of interest.\n",
    "\n",
    "These two major transfer learning scenarios look as follows:\n",
    "\n",
    "    Finetuning the convnet: Instead of random initializaion, we initialize the network with a pretrained network, like the one that is trained on imagenet 1000 dataset. Rest of the training looks as usual.\n",
    "    ConvNet as fixed feature extractor: Here, we will freeze the weights for all of the network except that of the final fully connected layer. This last fully connected layer is replaced with a new one with random weights and only this layer is trained.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# License: BSD\n",
    "# Author: Sasank Chilamkurthy\n",
    "\n",
    "from __future__ import print_function, division\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torch.optim import lr_scheduler\n",
    "import numpy as np\n",
    "import torchvision\n",
    "from torchvision import datasets, models, transforms\n",
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "import os\n",
    "import copy\n",
    "\n",
    "plt.ion()   # interactive mode"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load Data\n",
    "\n",
    "We will use torchvision and torch.utils.data packages for loading the data.\n",
    "\n",
    "The problem we’re going to solve today is to train a model to classify ants and bees. We have about 120 training images each for ants and bees. There are 75 validation images for each class. Usually, this is a very small dataset to generalize upon, if trained from scratch. Since we are using transfer learning, we should be able to generalize reasonably well.\n",
    "\n",
    "This dataset is a very small subset of imagenet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data augmentation and normalization for training\n",
    "# Just normalization for validation\n",
    "data_transforms = {\n",
    "    'train': transforms.Compose([\n",
    "        transforms.RandomResizedCrop(224),\n",
    "        transforms.RandomHorizontalFlip(),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
    "    ]),\n",
    "    'val': transforms.Compose([\n",
    "        transforms.Resize(256),\n",
    "        transforms.CenterCrop(224),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
    "    ]),\n",
    "}\n",
    "\n",
    "data_dir = '/home/jinbo/gitme/dl-algorithm-coding/data/bee'\n",
    "image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x),\n",
    "                                          data_transforms[x])\n",
    "                  for x in ['train', 'val']}\n",
    "dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=16,\n",
    "                                             shuffle=True, num_workers=4)\n",
    "              for x in ['train', 'val']}\n",
    "dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}\n",
    "class_names = image_datasets['train'].classes\n",
    "\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualize a few images\n",
    "\n",
    "Let’s visualize a few training images so as to understand the data augmentations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def imshow(inp, title=None):\n",
    "    \"\"\"Imshow for Tensor.\"\"\"\n",
    "    inp = inp.numpy().transpose((1, 2, 0))\n",
    "    mean = np.array([0.485, 0.456, 0.406])\n",
    "    std = np.array([0.229, 0.224, 0.225])\n",
    "    inp = std * inp + mean\n",
    "    inp = np.clip(inp, 0, 1)\n",
    "    plt.imshow(inp)\n",
    "    if title is not None:\n",
    "        plt.title(title)\n",
    "    plt.pause(0.001)  # pause a bit so that plots are updated\n",
    "\n",
    "\n",
    "# Get a batch of training data\n",
    "inputs, classes = next(iter(dataloaders['train']))\n",
    "\n",
    "# Make a grid from batch\n",
    "out = torchvision.utils.make_grid(inputs)\n",
    "\n",
    "imshow(out, title=[class_names[x] for x in classes])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Training the model\n",
    "\n",
    "Now, let’s write a general function to train a model. Here, we will illustrate:\n",
    "\n",
    "    Scheduling the learning rate\n",
    "    Saving the best model\n",
    "\n",
    "In the following, parameter scheduler is an LR scheduler object from torch.optim.lr_scheduler."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_model(model, criterion, optimizer, scheduler, num_epochs=25):\n",
    "    since = time.time()\n",
    "    \n",
    "    best_model_wts = copy.deepcopy(model.state_dict())\n",
    "    best_acc = 0.0\n",
    "\n",
    "    for epoch in range(num_epochs):\n",
    "        print('Epoch {}/{}'.format(epoch, num_epochs - 1))\n",
    "        print('-' * 10)\n",
    "        # Each epoch has a training and validation phase\n",
    "        for phase in ['train', 'val']:\n",
    "            if phase == 'train':\n",
    "                scheduler.step()\n",
    "                model.train()  # Set model to training mode\n",
    "            else:\n",
    "                model.eval()   # Set model to evaluate mode\n",
    "\n",
    "            running_loss = 0.0\n",
    "            running_corrects = 0\n",
    "\n",
    "            # Iterate over data.\n",
    "            for inputs, labels in dataloaders[phase]:\n",
    "                inputs = inputs.to(device)\n",
    "                labels = labels.to(device)\n",
    "\n",
    "                # zero the parameter gradients\n",
    "                optimizer.zero_grad()\n",
    "\n",
    "                # forward\n",
    "                # track history if only in train\n",
    "                with torch.set_grad_enabled(phase == 'train'):\n",
    "                    outputs = model(inputs)\n",
    "                    # output 为 bs × 2的 tensor，但值有正负且绝对值可大于1\n",
    "                    _, preds = torch.max(outputs, 1)\n",
    "                    loss = criterion(outputs, labels)\n",
    "\n",
    "                    # backward + optimize only if in training phase\n",
    "                    if phase == 'train':\n",
    "                        loss.backward()\n",
    "                        optimizer.step()\n",
    "\n",
    "                # statistics\n",
    "                running_loss += loss.item() * inputs.size(0)\n",
    "                running_corrects += torch.sum(preds == labels.data)\n",
    "\n",
    "            epoch_loss = running_loss / dataset_sizes[phase]\n",
    "            epoch_acc = running_corrects.double() / dataset_sizes[phase]\n",
    "\n",
    "            \n",
    "            if epoch % 1 == 0:\n",
    "                \n",
    "                print('{} Loss: {:.4f} Acc: {:.4f}'.format(\n",
    "                phase, epoch_loss, epoch_acc))\n",
    "                time_elapsed = time.time() - since\n",
    "                print('has spend time {:.0f}m {:.0f}s/n'.format(\n",
    "                    time_elapsed // 60, time_elapsed % 60))\n",
    "\n",
    "            # deep copy the model\n",
    "            if phase == 'val' and epoch_acc > best_acc:\n",
    "                best_acc = epoch_acc\n",
    "                best_model_wts = copy.deepcopy(model.state_dict())\n",
    "\n",
    "        print()\n",
    "\n",
    "    time_elapsed = time.time() - since\n",
    "    print('Training complete in {:.0f}m {:.0f}s'.format(\n",
    "        time_elapsed // 60, time_elapsed % 60))\n",
    "    print('Best val Acc: {:4f}'.format(best_acc))\n",
    "\n",
    "    # load best model weights\n",
    "    model.load_state_dict(best_model_wts)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualizing the model predictions\n",
    "\n",
    "Generic function to display predictions for a few images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def visualize_model(model, num_images=6):\n",
    "    was_training = model.training\n",
    "    model.eval()\n",
    "    images_so_far = 0\n",
    "    fig = plt.figure()\n",
    "\n",
    "    with torch.no_grad():\n",
    "        for i, (inputs, labels) in enumerate(dataloaders['val']):\n",
    "            inputs = inputs.to(device)\n",
    "            labels = labels.to(device)\n",
    "\n",
    "            outputs = model(inputs)\n",
    "            _, preds = torch.max(outputs, 1)\n",
    "\n",
    "            for j in range(inputs.size()[0]):\n",
    "                images_so_far += 1\n",
    "                ax = plt.subplot(num_images//2, 2, images_so_far)\n",
    "                ax.axis('off')\n",
    "                ax.set_title('predicted: {}'.format(class_names[preds[j]]))\n",
    "                imshow(inputs.cpu().data[j])\n",
    "\n",
    "                if images_so_far == num_images:\n",
    "                    model.train(mode=was_training)\n",
    "                    return\n",
    "        model.train(mode=was_training)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finetuning the convnet\n",
    "\n",
    "Load a pretrained model and reset final fully connected layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_ft = models.resnet18(pretrained=False)\n",
    "#model_ft = models.resnet18(pretrained=True)\n",
    "num_ftrs = model_ft.fc.in_features\n",
    "model_ft.fc = nn.Linear(num_ftrs, 2)\n",
    "\n",
    "model_ft = model_ft.to(device)\n",
    "\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "\n",
    "# Observe that all parameters are being optimized\n",
    "optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.005, momentum=0.9)\n",
    "\n",
    "# Decay LR by a factor of 0.1 every 7 epochs\n",
    "exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train and evaluate\n",
    "\n",
    "It should take around 15-25 min on CPU. On GPU though, it takes less than a minute."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0/9999\n",
      "----------\n",
      "train Loss: 0.7245 Acc: 0.5615\n",
      "has spend time 0m 2s/n\n",
      "val Loss: 0.8375 Acc: 0.4771\n",
      "has spend time 0m 2s/n\n",
      "\n",
      "Epoch 1/9999\n",
      "----------\n",
      "train Loss: 0.8308 Acc: 0.5369\n",
      "has spend time 0m 4s/n\n",
      "val Loss: 2.4359 Acc: 0.5425\n",
      "has spend time 0m 4s/n\n",
      "\n",
      "Epoch 2/9999\n",
      "----------\n",
      "train Loss: 0.7874 Acc: 0.6107\n",
      "has spend time 0m 6s/n\n",
      "val Loss: 0.6180 Acc: 0.7059\n",
      "has spend time 0m 6s/n\n",
      "\n",
      "Epoch 3/9999\n",
      "----------\n",
      "train Loss: 0.6847 Acc: 0.6148\n",
      "has spend time 0m 8s/n\n",
      "val Loss: 0.8226 Acc: 0.6209\n",
      "has spend time 0m 8s/n\n",
      "\n",
      "Epoch 4/9999\n",
      "----------\n",
      "train Loss: 0.7002 Acc: 0.6393\n",
      "has spend time 0m 10s/n\n",
      "val Loss: 4.0358 Acc: 0.4575\n",
      "has spend time 0m 10s/n\n",
      "\n",
      "Epoch 5/9999\n",
      "----------\n",
      "train Loss: 0.9617 Acc: 0.6189\n",
      "has spend time 0m 12s/n\n",
      "val Loss: 1.4500 Acc: 0.6340\n",
      "has spend time 0m 13s/n\n",
      "\n",
      "Epoch 6/9999\n",
      "----------\n",
      "train Loss: 0.7830 Acc: 0.5943\n",
      "has spend time 0m 14s/n\n",
      "val Loss: 1.0988 Acc: 0.4967\n",
      "has spend time 0m 15s/n\n",
      "\n",
      "Epoch 7/9999\n",
      "----------\n",
      "train Loss: 0.6798 Acc: 0.6434\n",
      "has spend time 0m 16s/n\n",
      "val Loss: 0.5888 Acc: 0.6993\n",
      "has spend time 0m 17s/n\n",
      "\n",
      "Epoch 8/9999\n",
      "----------\n",
      "train Loss: 0.5678 Acc: 0.6926\n",
      "has spend time 0m 19s/n\n",
      "val Loss: 0.5891 Acc: 0.6797\n",
      "has spend time 0m 19s/n\n",
      "\n",
      "Epoch 9/9999\n",
      "----------\n",
      "train Loss: 0.6047 Acc: 0.6762\n",
      "has spend time 0m 21s/n\n",
      "val Loss: 0.5629 Acc: 0.7124\n",
      "has spend time 0m 22s/n\n",
      "\n",
      "Epoch 10/9999\n",
      "----------\n",
      "train Loss: 0.5579 Acc: 0.6639\n",
      "has spend time 0m 23s/n\n",
      "val Loss: 0.5640 Acc: 0.6993\n",
      "has spend time 0m 24s/n\n",
      "\n",
      "Epoch 11/9999\n",
      "----------\n",
      "train Loss: 0.5679 Acc: 0.7008\n",
      "has spend time 0m 26s/n\n",
      "val Loss: 0.5618 Acc: 0.7190\n",
      "has spend time 0m 26s/n\n",
      "\n",
      "Epoch 12/9999\n",
      "----------\n",
      "train Loss: 0.5291 Acc: 0.7500\n",
      "has spend time 0m 28s/n\n",
      "val Loss: 0.5653 Acc: 0.6928\n",
      "has spend time 0m 28s/n\n",
      "\n",
      "Epoch 13/9999\n",
      "----------\n",
      "train Loss: 0.5151 Acc: 0.7459\n",
      "has spend time 0m 30s/n\n",
      "val Loss: 0.5442 Acc: 0.7190\n",
      "has spend time 0m 30s/n\n",
      "\n",
      "Epoch 14/9999\n",
      "----------\n",
      "train Loss: 0.5176 Acc: 0.7582\n",
      "has spend time 0m 32s/n\n",
      "val Loss: 0.5620 Acc: 0.7059\n",
      "has spend time 0m 32s/n\n",
      "\n",
      "Epoch 15/9999\n",
      "----------\n",
      "train Loss: 0.5267 Acc: 0.7131\n",
      "has spend time 0m 34s/n\n",
      "val Loss: 0.5567 Acc: 0.6993\n",
      "has spend time 0m 34s/n\n",
      "\n",
      "Epoch 16/9999\n",
      "----------\n",
      "train Loss: 0.5495 Acc: 0.6844\n",
      "has spend time 0m 36s/n\n",
      "val Loss: 0.5525 Acc: 0.7124\n",
      "has spend time 0m 37s/n\n",
      "\n",
      "Epoch 17/9999\n",
      "----------\n",
      "train Loss: 0.5326 Acc: 0.6967\n",
      "has spend time 0m 38s/n\n",
      "val Loss: 0.5514 Acc: 0.7190\n",
      "has spend time 0m 39s/n\n",
      "\n",
      "Epoch 18/9999\n",
      "----------\n",
      "train Loss: 0.4954 Acc: 0.7459\n",
      "has spend time 0m 40s/n\n",
      "val Loss: 0.5556 Acc: 0.7124\n",
      "has spend time 0m 41s/n\n",
      "\n",
      "Epoch 19/9999\n",
      "----------\n",
      "train Loss: 0.5022 Acc: 0.7418\n",
      "has spend time 0m 43s/n\n",
      "val Loss: 0.5614 Acc: 0.6863\n",
      "has spend time 0m 43s/n\n",
      "\n",
      "Epoch 20/9999\n",
      "----------\n",
      "train Loss: 0.4993 Acc: 0.7336\n",
      "has spend time 0m 45s/n\n",
      "val Loss: 0.5693 Acc: 0.6928\n",
      "has spend time 0m 45s/n\n",
      "\n",
      "Epoch 21/9999\n",
      "----------\n",
      "train Loss: 0.4929 Acc: 0.7213\n",
      "has spend time 0m 47s/n\n",
      "val Loss: 0.5484 Acc: 0.7124\n",
      "has spend time 0m 47s/n\n",
      "\n",
      "Epoch 22/9999\n",
      "----------\n",
      "train Loss: 0.5335 Acc: 0.7090\n",
      "has spend time 0m 49s/n\n",
      "val Loss: 0.5474 Acc: 0.7059\n",
      "has spend time 0m 49s/n\n",
      "\n",
      "Epoch 23/9999\n",
      "----------\n",
      "train Loss: 0.5226 Acc: 0.7090\n",
      "has spend time 0m 51s/n\n",
      "val Loss: 0.5600 Acc: 0.6993\n",
      "has spend time 0m 52s/n\n",
      "\n",
      "Epoch 24/9999\n",
      "----------\n",
      "train Loss: 0.4940 Acc: 0.7623\n",
      "has spend time 0m 53s/n\n",
      "val Loss: 0.5446 Acc: 0.7124\n",
      "has spend time 0m 54s/n\n",
      "\n",
      "Epoch 25/9999\n",
      "----------\n",
      "train Loss: 0.5207 Acc: 0.7295\n",
      "has spend time 0m 55s/n\n",
      "val Loss: 0.5540 Acc: 0.6993\n",
      "has spend time 0m 56s/n\n",
      "\n",
      "Epoch 26/9999\n",
      "----------\n",
      "train Loss: 0.4947 Acc: 0.7418\n",
      "has spend time 0m 57s/n\n",
      "val Loss: 0.5528 Acc: 0.6993\n",
      "has spend time 0m 58s/n\n",
      "\n",
      "Epoch 27/9999\n",
      "----------\n",
      "train Loss: 0.5053 Acc: 0.7213\n",
      "has spend time 0m 59s/n\n",
      "val Loss: 0.5641 Acc: 0.6993\n",
      "has spend time 1m 0s/n\n",
      "\n",
      "Epoch 28/9999\n",
      "----------\n",
      "train Loss: 0.4857 Acc: 0.7869\n",
      "has spend time 1m 2s/n\n",
      "val Loss: 0.5448 Acc: 0.7190\n",
      "has spend time 1m 2s/n\n",
      "\n",
      "Epoch 29/9999\n",
      "----------\n",
      "train Loss: 0.4964 Acc: 0.7623\n",
      "has spend time 1m 4s/n\n",
      "val Loss: 0.5450 Acc: 0.7190\n",
      "has spend time 1m 4s/n\n",
      "\n",
      "Epoch 30/9999\n",
      "----------\n",
      "train Loss: 0.5007 Acc: 0.7377\n",
      "has spend time 1m 6s/n\n",
      "val Loss: 0.5613 Acc: 0.7059\n",
      "has spend time 1m 6s/n\n",
      "\n",
      "Epoch 31/9999\n",
      "----------\n",
      "train Loss: 0.5142 Acc: 0.7500\n",
      "has spend time 1m 8s/n\n",
      "val Loss: 0.5514 Acc: 0.7059\n",
      "has spend time 1m 8s/n\n",
      "\n",
      "Epoch 32/9999\n",
      "----------\n",
      "train Loss: 0.5305 Acc: 0.7295\n",
      "has spend time 1m 10s/n\n",
      "val Loss: 0.5640 Acc: 0.6993\n",
      "has spend time 1m 10s/n\n",
      "\n",
      "Epoch 33/9999\n",
      "----------\n",
      "train Loss: 0.5162 Acc: 0.7459\n",
      "has spend time 1m 12s/n\n",
      "val Loss: 0.5585 Acc: 0.6993\n",
      "has spend time 1m 12s/n\n",
      "\n",
      "Epoch 34/9999\n",
      "----------\n",
      "train Loss: 0.5267 Acc: 0.7172\n",
      "has spend time 1m 14s/n\n",
      "val Loss: 0.5470 Acc: 0.7059\n",
      "has spend time 1m 15s/n\n",
      "\n",
      "Epoch 35/9999\n",
      "----------\n",
      "train Loss: 0.4945 Acc: 0.7623\n",
      "has spend time 1m 16s/n\n",
      "val Loss: 0.5438 Acc: 0.7124\n",
      "has spend time 1m 17s/n\n",
      "\n",
      "Epoch 36/9999\n",
      "----------\n",
      "train Loss: 0.5102 Acc: 0.7254\n",
      "has spend time 1m 18s/n\n",
      "val Loss: 0.5709 Acc: 0.6863\n",
      "has spend time 1m 19s/n\n",
      "\n",
      "Epoch 37/9999\n",
      "----------\n",
      "train Loss: 0.4900 Acc: 0.7459\n",
      "has spend time 1m 21s/n\n",
      "val Loss: 0.5436 Acc: 0.7059\n",
      "has spend time 1m 22s/n\n",
      "\n",
      "Epoch 38/9999\n",
      "----------\n",
      "train Loss: 0.5065 Acc: 0.7951\n",
      "has spend time 1m 23s/n\n",
      "val Loss: 0.5594 Acc: 0.7190\n",
      "has spend time 1m 24s/n\n",
      "\n",
      "Epoch 39/9999\n",
      "----------\n",
      "train Loss: 0.5104 Acc: 0.7213\n",
      "has spend time 1m 25s/n\n",
      "val Loss: 0.5448 Acc: 0.7059\n",
      "has spend time 1m 26s/n\n",
      "\n",
      "Epoch 40/9999\n",
      "----------\n",
      "train Loss: 0.5288 Acc: 0.7213\n",
      "has spend time 1m 27s/n\n",
      "val Loss: 0.5445 Acc: 0.7124\n",
      "has spend time 1m 28s/n\n",
      "\n",
      "Epoch 41/9999\n",
      "----------\n",
      "train Loss: 0.4810 Acc: 0.7746\n",
      "has spend time 1m 29s/n\n",
      "val Loss: 0.5431 Acc: 0.7255\n",
      "has spend time 1m 30s/n\n",
      "\n",
      "Epoch 42/9999\n",
      "----------\n",
      "train Loss: 0.4806 Acc: 0.7500\n",
      "has spend time 1m 31s/n\n",
      "val Loss: 0.5473 Acc: 0.7059\n",
      "has spend time 1m 32s/n\n",
      "\n",
      "Epoch 43/9999\n",
      "----------\n",
      "train Loss: 0.5065 Acc: 0.7582\n",
      "has spend time 1m 34s/n\n",
      "val Loss: 0.5521 Acc: 0.7059\n",
      "has spend time 1m 35s/n\n",
      "\n",
      "Epoch 44/9999\n",
      "----------\n",
      "train Loss: 0.5016 Acc: 0.7582\n",
      "has spend time 1m 36s/n\n",
      "val Loss: 0.5643 Acc: 0.6993\n",
      "has spend time 1m 37s/n\n",
      "\n",
      "Epoch 45/9999\n",
      "----------\n",
      "train Loss: 0.5003 Acc: 0.7336\n",
      "has spend time 1m 38s/n\n",
      "val Loss: 0.5533 Acc: 0.7059\n",
      "has spend time 1m 39s/n\n",
      "\n",
      "Epoch 46/9999\n",
      "----------\n",
      "train Loss: 0.4813 Acc: 0.7459\n",
      "has spend time 1m 41s/n\n",
      "val Loss: 0.5493 Acc: 0.7190\n",
      "has spend time 1m 42s/n\n",
      "\n",
      "Epoch 47/9999\n",
      "----------\n",
      "train Loss: 0.4899 Acc: 0.7541\n",
      "has spend time 1m 43s/n\n",
      "val Loss: 0.5398 Acc: 0.7320\n",
      "has spend time 1m 44s/n\n",
      "\n",
      "Epoch 48/9999\n",
      "----------\n",
      "train Loss: 0.4992 Acc: 0.7541\n",
      "has spend time 1m 46s/n\n",
      "val Loss: 0.5463 Acc: 0.7124\n",
      "has spend time 1m 46s/n\n",
      "\n",
      "Epoch 49/9999\n",
      "----------\n",
      "train Loss: 0.5457 Acc: 0.7213\n",
      "has spend time 1m 48s/n\n",
      "val Loss: 0.5558 Acc: 0.7059\n",
      "has spend time 1m 48s/n\n",
      "\n",
      "Epoch 50/9999\n",
      "----------\n",
      "train Loss: 0.4735 Acc: 0.7828\n",
      "has spend time 1m 50s/n\n",
      "val Loss: 0.5515 Acc: 0.7059\n",
      "has spend time 1m 50s/n\n",
      "\n",
      "Epoch 51/9999\n",
      "----------\n",
      "train Loss: 0.4850 Acc: 0.7459\n",
      "has spend time 1m 52s/n\n",
      "val Loss: 0.5507 Acc: 0.7124\n",
      "has spend time 1m 52s/n\n",
      "\n",
      "Epoch 52/9999\n",
      "----------\n",
      "train Loss: 0.5039 Acc: 0.7295\n",
      "has spend time 1m 54s/n\n",
      "val Loss: 0.5441 Acc: 0.7190\n",
      "has spend time 1m 55s/n\n",
      "\n",
      "Epoch 53/9999\n",
      "----------\n",
      "train Loss: 0.5195 Acc: 0.7336\n",
      "has spend time 1m 56s/n\n",
      "val Loss: 0.5421 Acc: 0.7190\n",
      "has spend time 1m 57s/n\n",
      "\n",
      "Epoch 54/9999\n",
      "----------\n",
      "train Loss: 0.5010 Acc: 0.7623\n",
      "has spend time 1m 58s/n\n",
      "val Loss: 0.5454 Acc: 0.7190\n",
      "has spend time 1m 59s/n\n",
      "\n",
      "Epoch 55/9999\n",
      "----------\n",
      "train Loss: 0.5356 Acc: 0.7295\n",
      "has spend time 1m 60s/n\n",
      "val Loss: 0.5615 Acc: 0.6928\n",
      "has spend time 2m 1s/n\n",
      "\n",
      "Epoch 56/9999\n",
      "----------\n",
      "train Loss: 0.5127 Acc: 0.7459\n",
      "has spend time 2m 2s/n\n",
      "val Loss: 0.5609 Acc: 0.6928\n",
      "has spend time 2m 3s/n\n",
      "\n",
      "Epoch 57/9999\n",
      "----------\n",
      "train Loss: 0.5110 Acc: 0.7254\n",
      "has spend time 2m 4s/n\n",
      "val Loss: 0.5352 Acc: 0.7255\n",
      "has spend time 2m 5s/n\n",
      "\n",
      "Epoch 58/9999\n",
      "----------\n",
      "train Loss: 0.5267 Acc: 0.7295\n",
      "has spend time 2m 6s/n\n",
      "val Loss: 0.5435 Acc: 0.7124\n",
      "has spend time 2m 7s/n\n",
      "\n",
      "Epoch 59/9999\n",
      "----------\n",
      "train Loss: 0.5253 Acc: 0.7295\n",
      "has spend time 2m 8s/n\n",
      "val Loss: 0.5502 Acc: 0.7124\n",
      "has spend time 2m 9s/n\n",
      "\n",
      "Epoch 60/9999\n",
      "----------\n",
      "train Loss: 0.4862 Acc: 0.7500\n",
      "has spend time 2m 10s/n\n",
      "val Loss: 0.5508 Acc: 0.7124\n",
      "has spend time 2m 11s/n\n",
      "\n",
      "Epoch 61/9999\n",
      "----------\n",
      "train Loss: 0.5053 Acc: 0.7336\n",
      "has spend time 2m 12s/n\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val Loss: 0.5516 Acc: 0.7124\n",
      "has spend time 2m 13s/n\n",
      "\n",
      "Epoch 62/9999\n",
      "----------\n",
      "train Loss: 0.4694 Acc: 0.7541\n",
      "has spend time 2m 14s/n\n",
      "val Loss: 0.5469 Acc: 0.7124\n",
      "has spend time 2m 15s/n\n",
      "\n",
      "Epoch 63/9999\n",
      "----------\n",
      "train Loss: 0.5091 Acc: 0.7295\n",
      "has spend time 2m 17s/n\n",
      "val Loss: 0.5566 Acc: 0.7124\n",
      "has spend time 2m 17s/n\n",
      "\n",
      "Epoch 64/9999\n",
      "----------\n",
      "train Loss: 0.5113 Acc: 0.7336\n",
      "has spend time 2m 19s/n\n",
      "val Loss: 0.5529 Acc: 0.7124\n",
      "has spend time 2m 20s/n\n",
      "\n",
      "Epoch 65/9999\n",
      "----------\n",
      "train Loss: 0.5372 Acc: 0.7377\n",
      "has spend time 2m 21s/n\n",
      "val Loss: 0.5538 Acc: 0.7059\n",
      "has spend time 2m 22s/n\n",
      "\n",
      "Epoch 66/9999\n",
      "----------\n",
      "train Loss: 0.5100 Acc: 0.7131\n",
      "has spend time 2m 24s/n\n",
      "val Loss: 0.5479 Acc: 0.7124\n",
      "has spend time 2m 24s/n\n",
      "\n",
      "Epoch 67/9999\n",
      "----------\n",
      "train Loss: 0.4948 Acc: 0.7582\n",
      "has spend time 2m 26s/n\n",
      "val Loss: 0.5487 Acc: 0.7124\n",
      "has spend time 2m 26s/n\n",
      "\n",
      "Epoch 68/9999\n",
      "----------\n",
      "train Loss: 0.5139 Acc: 0.7664\n",
      "has spend time 2m 28s/n\n",
      "val Loss: 0.5471 Acc: 0.7190\n",
      "has spend time 2m 28s/n\n",
      "\n",
      "Epoch 69/9999\n",
      "----------\n",
      "train Loss: 0.5307 Acc: 0.7336\n",
      "has spend time 2m 30s/n\n",
      "val Loss: 0.5528 Acc: 0.7190\n",
      "has spend time 2m 30s/n\n",
      "\n",
      "Epoch 70/9999\n",
      "----------\n",
      "train Loss: 0.4932 Acc: 0.7418\n",
      "has spend time 2m 32s/n\n",
      "val Loss: 0.5545 Acc: 0.7059\n",
      "has spend time 2m 32s/n\n",
      "\n",
      "Epoch 71/9999\n",
      "----------\n",
      "train Loss: 0.5045 Acc: 0.7500\n",
      "has spend time 2m 34s/n\n",
      "val Loss: 0.5563 Acc: 0.7190\n",
      "has spend time 2m 35s/n\n",
      "\n",
      "Epoch 72/9999\n",
      "----------\n",
      "train Loss: 0.4985 Acc: 0.7336\n",
      "has spend time 2m 36s/n\n",
      "val Loss: 0.5623 Acc: 0.6993\n",
      "has spend time 2m 37s/n\n",
      "\n",
      "Epoch 73/9999\n",
      "----------\n",
      "train Loss: 0.5288 Acc: 0.7213\n",
      "has spend time 2m 39s/n\n",
      "val Loss: 0.5507 Acc: 0.7124\n",
      "has spend time 2m 39s/n\n",
      "\n",
      "Epoch 74/9999\n",
      "----------\n",
      "train Loss: 0.5148 Acc: 0.7131\n",
      "has spend time 2m 41s/n\n",
      "val Loss: 0.5563 Acc: 0.7059\n",
      "has spend time 2m 42s/n\n",
      "\n",
      "Epoch 75/9999\n",
      "----------\n",
      "train Loss: 0.5050 Acc: 0.7500\n",
      "has spend time 2m 43s/n\n",
      "val Loss: 0.5573 Acc: 0.6993\n",
      "has spend time 2m 44s/n\n",
      "\n",
      "Epoch 76/9999\n",
      "----------\n",
      "train Loss: 0.5094 Acc: 0.7295\n",
      "has spend time 2m 45s/n\n",
      "val Loss: 0.5454 Acc: 0.7124\n",
      "has spend time 2m 46s/n\n",
      "\n",
      "Epoch 77/9999\n",
      "----------\n",
      "train Loss: 0.4971 Acc: 0.7336\n",
      "has spend time 2m 48s/n\n",
      "val Loss: 0.5458 Acc: 0.7124\n",
      "has spend time 2m 48s/n\n",
      "\n",
      "Epoch 78/9999\n",
      "----------\n",
      "train Loss: 0.4807 Acc: 0.7869\n",
      "has spend time 2m 50s/n\n",
      "val Loss: 0.5549 Acc: 0.6928\n",
      "has spend time 2m 50s/n\n",
      "\n",
      "Epoch 79/9999\n",
      "----------\n",
      "train Loss: 0.4893 Acc: 0.7582\n",
      "has spend time 2m 52s/n\n",
      "val Loss: 0.5608 Acc: 0.7059\n",
      "has spend time 2m 52s/n\n",
      "\n",
      "Epoch 80/9999\n",
      "----------\n",
      "train Loss: 0.5091 Acc: 0.7459\n",
      "has spend time 2m 54s/n\n",
      "val Loss: 0.5421 Acc: 0.7124\n",
      "has spend time 2m 54s/n\n",
      "\n",
      "Epoch 81/9999\n",
      "----------\n",
      "train Loss: 0.5144 Acc: 0.7377\n",
      "has spend time 2m 56s/n\n",
      "val Loss: 0.5502 Acc: 0.7124\n",
      "has spend time 2m 57s/n\n",
      "\n",
      "Epoch 82/9999\n",
      "----------\n",
      "train Loss: 0.4987 Acc: 0.7377\n",
      "has spend time 2m 58s/n\n",
      "val Loss: 0.5553 Acc: 0.6993\n",
      "has spend time 2m 59s/n\n",
      "\n",
      "Epoch 83/9999\n",
      "----------\n",
      "train Loss: 0.5180 Acc: 0.7172\n",
      "has spend time 3m 0s/n\n",
      "val Loss: 0.5564 Acc: 0.6993\n",
      "has spend time 3m 1s/n\n",
      "\n",
      "Epoch 84/9999\n",
      "----------\n",
      "train Loss: 0.4962 Acc: 0.7377\n",
      "has spend time 3m 2s/n\n",
      "val Loss: 0.5445 Acc: 0.7255\n",
      "has spend time 3m 3s/n\n",
      "\n",
      "Epoch 85/9999\n",
      "----------\n",
      "train Loss: 0.4783 Acc: 0.7418\n",
      "has spend time 3m 4s/n\n",
      "val Loss: 0.5579 Acc: 0.7059\n",
      "has spend time 3m 5s/n\n",
      "\n",
      "Epoch 86/9999\n",
      "----------\n",
      "train Loss: 0.5059 Acc: 0.7623\n",
      "has spend time 3m 7s/n\n",
      "val Loss: 0.5496 Acc: 0.7059\n",
      "has spend time 3m 7s/n\n",
      "\n",
      "Epoch 87/9999\n",
      "----------\n",
      "train Loss: 0.5068 Acc: 0.7500\n",
      "has spend time 3m 9s/n\n",
      "val Loss: 0.5466 Acc: 0.7190\n",
      "has spend time 3m 9s/n\n",
      "\n",
      "Epoch 88/9999\n",
      "----------\n",
      "train Loss: 0.5313 Acc: 0.7295\n",
      "has spend time 3m 11s/n\n",
      "val Loss: 0.5480 Acc: 0.7255\n",
      "has spend time 3m 12s/n\n",
      "\n",
      "Epoch 89/9999\n",
      "----------\n",
      "train Loss: 0.5225 Acc: 0.7295\n",
      "has spend time 3m 13s/n\n",
      "val Loss: 0.5567 Acc: 0.7059\n",
      "has spend time 3m 14s/n\n",
      "\n",
      "Epoch 90/9999\n",
      "----------\n",
      "train Loss: 0.5230 Acc: 0.7213\n",
      "has spend time 3m 15s/n\n",
      "val Loss: 0.5460 Acc: 0.7190\n",
      "has spend time 3m 16s/n\n",
      "\n",
      "Epoch 91/9999\n",
      "----------\n",
      "train Loss: 0.5096 Acc: 0.7500\n",
      "has spend time 3m 17s/n\n",
      "val Loss: 0.5635 Acc: 0.6993\n",
      "has spend time 3m 18s/n\n",
      "\n",
      "Epoch 92/9999\n",
      "----------\n",
      "train Loss: 0.5024 Acc: 0.7623\n",
      "has spend time 3m 19s/n\n",
      "val Loss: 0.5642 Acc: 0.6863\n",
      "has spend time 3m 20s/n\n",
      "\n",
      "Epoch 93/9999\n",
      "----------\n",
      "train Loss: 0.5513 Acc: 0.7049\n",
      "has spend time 3m 22s/n\n",
      "val Loss: 0.5458 Acc: 0.7059\n",
      "has spend time 3m 22s/n\n",
      "\n",
      "Epoch 94/9999\n",
      "----------\n",
      "train Loss: 0.5297 Acc: 0.7049\n",
      "has spend time 3m 24s/n\n",
      "val Loss: 0.5608 Acc: 0.6928\n",
      "has spend time 3m 24s/n\n",
      "\n",
      "Epoch 95/9999\n",
      "----------\n",
      "train Loss: 0.4917 Acc: 0.7377\n",
      "has spend time 3m 26s/n\n",
      "val Loss: 0.5442 Acc: 0.7124\n",
      "has spend time 3m 26s/n\n",
      "\n",
      "Epoch 96/9999\n",
      "----------\n",
      "train Loss: 0.5554 Acc: 0.7295\n",
      "has spend time 3m 28s/n\n",
      "val Loss: 0.5525 Acc: 0.7124\n",
      "has spend time 3m 28s/n\n",
      "\n",
      "Epoch 97/9999\n",
      "----------\n",
      "train Loss: 0.5203 Acc: 0.7377\n",
      "has spend time 3m 30s/n\n",
      "val Loss: 0.5467 Acc: 0.7059\n",
      "has spend time 3m 31s/n\n",
      "\n",
      "Epoch 98/9999\n",
      "----------\n",
      "train Loss: 0.5005 Acc: 0.7213\n",
      "has spend time 3m 32s/n\n",
      "val Loss: 0.5466 Acc: 0.7124\n",
      "has spend time 3m 33s/n\n",
      "\n",
      "Epoch 99/9999\n",
      "----------\n",
      "train Loss: 0.5109 Acc: 0.7213\n",
      "has spend time 3m 35s/n\n",
      "val Loss: 0.5430 Acc: 0.7190\n",
      "has spend time 3m 35s/n\n",
      "\n",
      "Epoch 100/9999\n",
      "----------\n",
      "train Loss: 0.5363 Acc: 0.7172\n",
      "has spend time 3m 37s/n\n",
      "val Loss: 0.5397 Acc: 0.7255\n",
      "has spend time 3m 37s/n\n",
      "\n",
      "Epoch 101/9999\n",
      "----------\n",
      "train Loss: 0.5462 Acc: 0.7008\n",
      "has spend time 3m 39s/n\n",
      "val Loss: 0.5448 Acc: 0.7124\n",
      "has spend time 3m 40s/n\n",
      "\n",
      "Epoch 102/9999\n",
      "----------\n",
      "train Loss: 0.5014 Acc: 0.7254\n",
      "has spend time 3m 41s/n\n",
      "val Loss: 0.5486 Acc: 0.6993\n",
      "has spend time 3m 42s/n\n",
      "\n",
      "Epoch 103/9999\n",
      "----------\n",
      "train Loss: 0.5237 Acc: 0.7131\n",
      "has spend time 3m 43s/n\n",
      "val Loss: 0.5408 Acc: 0.7190\n",
      "has spend time 3m 44s/n\n",
      "\n",
      "Epoch 104/9999\n",
      "----------\n",
      "train Loss: 0.4920 Acc: 0.7664\n",
      "has spend time 3m 45s/n\n",
      "val Loss: 0.5392 Acc: 0.7124\n",
      "has spend time 3m 46s/n\n",
      "\n",
      "Epoch 105/9999\n",
      "----------\n",
      "train Loss: 0.5125 Acc: 0.7131\n",
      "has spend time 3m 47s/n\n",
      "val Loss: 0.5466 Acc: 0.7124\n",
      "has spend time 3m 48s/n\n",
      "\n",
      "Epoch 106/9999\n",
      "----------\n",
      "train Loss: 0.5415 Acc: 0.7008\n",
      "has spend time 3m 49s/n\n",
      "val Loss: 0.5447 Acc: 0.7190\n",
      "has spend time 3m 50s/n\n",
      "\n",
      "Epoch 107/9999\n",
      "----------\n",
      "train Loss: 0.4688 Acc: 0.7705\n",
      "has spend time 3m 52s/n\n",
      "val Loss: 0.5458 Acc: 0.7059\n",
      "has spend time 3m 53s/n\n",
      "\n",
      "Epoch 108/9999\n",
      "----------\n",
      "train Loss: 0.4864 Acc: 0.7787\n",
      "has spend time 3m 54s/n\n",
      "val Loss: 0.5501 Acc: 0.7190\n",
      "has spend time 3m 55s/n\n",
      "\n",
      "Epoch 109/9999\n",
      "----------\n",
      "train Loss: 0.5387 Acc: 0.6967\n",
      "has spend time 3m 56s/n\n",
      "val Loss: 0.5430 Acc: 0.7255\n",
      "has spend time 3m 57s/n\n",
      "\n",
      "Epoch 110/9999\n",
      "----------\n",
      "train Loss: 0.5045 Acc: 0.7623\n",
      "has spend time 3m 58s/n\n",
      "val Loss: 0.5550 Acc: 0.7124\n",
      "has spend time 3m 59s/n\n",
      "\n",
      "Epoch 111/9999\n",
      "----------\n",
      "train Loss: 0.4761 Acc: 0.7418\n",
      "has spend time 4m 1s/n\n",
      "val Loss: 0.5525 Acc: 0.7059\n",
      "has spend time 4m 1s/n\n",
      "\n",
      "Epoch 112/9999\n",
      "----------\n",
      "train Loss: 0.5150 Acc: 0.6926\n",
      "has spend time 4m 3s/n\n",
      "val Loss: 0.5489 Acc: 0.7190\n",
      "has spend time 4m 3s/n\n",
      "\n",
      "Epoch 113/9999\n",
      "----------\n",
      "train Loss: 0.4901 Acc: 0.7541\n",
      "has spend time 4m 5s/n\n",
      "val Loss: 0.5505 Acc: 0.7124\n",
      "has spend time 4m 6s/n\n",
      "\n",
      "Epoch 114/9999\n",
      "----------\n",
      "train Loss: 0.5254 Acc: 0.7336\n",
      "has spend time 4m 7s/n\n",
      "val Loss: 0.5462 Acc: 0.7124\n",
      "has spend time 4m 8s/n\n",
      "\n",
      "Epoch 115/9999\n",
      "----------\n",
      "train Loss: 0.4829 Acc: 0.7459\n",
      "has spend time 4m 9s/n\n",
      "val Loss: 0.5381 Acc: 0.7190\n",
      "has spend time 4m 10s/n\n",
      "\n",
      "Epoch 116/9999\n",
      "----------\n",
      "train Loss: 0.5080 Acc: 0.7295\n",
      "has spend time 4m 12s/n\n",
      "val Loss: 0.5465 Acc: 0.7124\n",
      "has spend time 4m 12s/n\n",
      "\n",
      "Epoch 117/9999\n",
      "----------\n",
      "train Loss: 0.5112 Acc: 0.7213\n",
      "has spend time 4m 14s/n\n",
      "val Loss: 0.5558 Acc: 0.7059\n",
      "has spend time 4m 14s/n\n",
      "\n",
      "Epoch 118/9999\n",
      "----------\n",
      "train Loss: 0.5092 Acc: 0.7705\n",
      "has spend time 4m 16s/n\n",
      "val Loss: 0.5535 Acc: 0.7124\n",
      "has spend time 4m 16s/n\n",
      "\n",
      "Epoch 119/9999\n",
      "----------\n",
      "train Loss: 0.5074 Acc: 0.7787\n",
      "has spend time 4m 18s/n\n",
      "val Loss: 0.5504 Acc: 0.7190\n",
      "has spend time 4m 18s/n\n",
      "\n",
      "Epoch 120/9999\n",
      "----------\n",
      "train Loss: 0.5053 Acc: 0.7582\n",
      "has spend time 4m 20s/n\n",
      "val Loss: 0.5490 Acc: 0.6928\n",
      "has spend time 4m 20s/n\n",
      "\n",
      "Epoch 121/9999\n",
      "----------\n",
      "train Loss: 0.5124 Acc: 0.7623\n",
      "has spend time 4m 22s/n\n",
      "val Loss: 0.5492 Acc: 0.7124\n",
      "has spend time 4m 22s/n\n",
      "\n",
      "Epoch 122/9999\n",
      "----------\n",
      "train Loss: 0.5071 Acc: 0.7295\n",
      "has spend time 4m 24s/n\n",
      "val Loss: 0.5494 Acc: 0.7124\n",
      "has spend time 4m 24s/n\n",
      "\n",
      "Epoch 123/9999\n",
      "----------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train Loss: 0.4916 Acc: 0.7418\n",
      "has spend time 4m 26s/n\n",
      "val Loss: 0.5490 Acc: 0.7124\n",
      "has spend time 4m 26s/n\n",
      "\n",
      "Epoch 124/9999\n",
      "----------\n",
      "train Loss: 0.4656 Acc: 0.7828\n",
      "has spend time 4m 28s/n\n",
      "val Loss: 0.5622 Acc: 0.6928\n",
      "has spend time 4m 29s/n\n",
      "\n",
      "Epoch 125/9999\n",
      "----------\n",
      "train Loss: 0.5184 Acc: 0.7254\n",
      "has spend time 4m 30s/n\n",
      "val Loss: 0.5455 Acc: 0.7190\n",
      "has spend time 4m 31s/n\n",
      "\n",
      "Epoch 126/9999\n",
      "----------\n",
      "train Loss: 0.4969 Acc: 0.7541\n",
      "has spend time 4m 32s/n\n",
      "val Loss: 0.5412 Acc: 0.7190\n",
      "has spend time 4m 33s/n\n",
      "\n",
      "Epoch 127/9999\n",
      "----------\n",
      "train Loss: 0.4910 Acc: 0.7336\n",
      "has spend time 4m 34s/n\n",
      "val Loss: 0.5457 Acc: 0.7059\n",
      "has spend time 4m 35s/n\n",
      "\n",
      "Epoch 128/9999\n",
      "----------\n",
      "train Loss: 0.5112 Acc: 0.7172\n",
      "has spend time 4m 36s/n\n",
      "val Loss: 0.5503 Acc: 0.6993\n",
      "has spend time 4m 37s/n\n",
      "\n",
      "Epoch 129/9999\n",
      "----------\n",
      "train Loss: 0.5235 Acc: 0.7172\n",
      "has spend time 4m 38s/n\n",
      "val Loss: 0.5486 Acc: 0.6928\n",
      "has spend time 4m 39s/n\n",
      "\n",
      "Epoch 130/9999\n",
      "----------\n",
      "train Loss: 0.5138 Acc: 0.7336\n",
      "has spend time 4m 41s/n\n",
      "val Loss: 0.5416 Acc: 0.7190\n",
      "has spend time 4m 41s/n\n",
      "\n",
      "Epoch 131/9999\n",
      "----------\n",
      "train Loss: 0.4666 Acc: 0.7787\n",
      "has spend time 4m 43s/n\n",
      "val Loss: 0.5437 Acc: 0.7190\n",
      "has spend time 4m 44s/n\n",
      "\n",
      "Epoch 132/9999\n",
      "----------\n",
      "train Loss: 0.5204 Acc: 0.7336\n",
      "has spend time 4m 45s/n\n",
      "val Loss: 0.5450 Acc: 0.7190\n",
      "has spend time 4m 46s/n\n",
      "\n",
      "Epoch 133/9999\n",
      "----------\n",
      "train Loss: 0.5031 Acc: 0.7213\n",
      "has spend time 4m 47s/n\n",
      "val Loss: 0.5485 Acc: 0.7059\n",
      "has spend time 4m 48s/n\n",
      "\n",
      "Epoch 134/9999\n",
      "----------\n",
      "train Loss: 0.4997 Acc: 0.7377\n",
      "has spend time 4m 49s/n\n",
      "val Loss: 0.5579 Acc: 0.6928\n",
      "has spend time 4m 50s/n\n",
      "\n",
      "Epoch 135/9999\n",
      "----------\n",
      "train Loss: 0.5168 Acc: 0.7295\n",
      "has spend time 4m 51s/n\n",
      "val Loss: 0.5551 Acc: 0.6993\n",
      "has spend time 4m 52s/n\n",
      "\n",
      "Epoch 136/9999\n",
      "----------\n",
      "train Loss: 0.5302 Acc: 0.7131\n",
      "has spend time 4m 53s/n\n",
      "val Loss: 0.5464 Acc: 0.7124\n",
      "has spend time 4m 54s/n\n",
      "\n",
      "Epoch 137/9999\n",
      "----------\n",
      "train Loss: 0.5310 Acc: 0.6926\n",
      "has spend time 4m 55s/n\n",
      "val Loss: 0.5463 Acc: 0.7059\n",
      "has spend time 4m 56s/n\n",
      "\n",
      "Epoch 138/9999\n",
      "----------\n",
      "train Loss: 0.5120 Acc: 0.7336\n",
      "has spend time 4m 58s/n\n",
      "val Loss: 0.5591 Acc: 0.7059\n",
      "has spend time 4m 59s/n\n",
      "\n",
      "Epoch 139/9999\n",
      "----------\n",
      "train Loss: 0.5179 Acc: 0.7049\n",
      "has spend time 5m 0s/n\n",
      "val Loss: 0.5691 Acc: 0.7059\n",
      "has spend time 5m 1s/n\n",
      "\n",
      "Epoch 140/9999\n",
      "----------\n",
      "train Loss: 0.5047 Acc: 0.7541\n",
      "has spend time 5m 2s/n\n",
      "val Loss: 0.5505 Acc: 0.7124\n",
      "has spend time 5m 3s/n\n",
      "\n",
      "Epoch 141/9999\n",
      "----------\n",
      "train Loss: 0.4792 Acc: 0.7623\n",
      "has spend time 5m 4s/n\n",
      "val Loss: 0.5454 Acc: 0.7190\n",
      "has spend time 5m 5s/n\n",
      "\n",
      "Epoch 142/9999\n",
      "----------\n",
      "train Loss: 0.5278 Acc: 0.6885\n",
      "has spend time 5m 6s/n\n",
      "val Loss: 0.5599 Acc: 0.6993\n",
      "has spend time 5m 7s/n\n",
      "\n",
      "Epoch 143/9999\n",
      "----------\n",
      "train Loss: 0.5079 Acc: 0.7254\n",
      "has spend time 5m 8s/n\n",
      "val Loss: 0.5696 Acc: 0.6993\n",
      "has spend time 5m 9s/n\n",
      "\n",
      "Epoch 144/9999\n",
      "----------\n",
      "train Loss: 0.5117 Acc: 0.7582\n",
      "has spend time 5m 11s/n\n",
      "val Loss: 0.5500 Acc: 0.7124\n",
      "has spend time 5m 12s/n\n",
      "\n",
      "Epoch 145/9999\n",
      "----------\n",
      "train Loss: 0.4999 Acc: 0.7377\n",
      "has spend time 5m 13s/n\n",
      "val Loss: 0.5632 Acc: 0.6993\n",
      "has spend time 5m 14s/n\n",
      "\n",
      "Epoch 146/9999\n",
      "----------\n",
      "train Loss: 0.4962 Acc: 0.7582\n",
      "has spend time 5m 15s/n\n",
      "val Loss: 0.5669 Acc: 0.6993\n",
      "has spend time 5m 16s/n\n",
      "\n",
      "Epoch 147/9999\n",
      "----------\n",
      "train Loss: 0.5101 Acc: 0.7295\n",
      "has spend time 5m 17s/n\n",
      "val Loss: 0.5578 Acc: 0.7124\n",
      "has spend time 5m 18s/n\n",
      "\n",
      "Epoch 148/9999\n",
      "----------\n",
      "train Loss: 0.5111 Acc: 0.7254\n",
      "has spend time 5m 19s/n\n",
      "val Loss: 0.5496 Acc: 0.7059\n",
      "has spend time 5m 20s/n\n",
      "\n",
      "Epoch 149/9999\n",
      "----------\n",
      "train Loss: 0.4718 Acc: 0.7705\n",
      "has spend time 5m 21s/n\n",
      "val Loss: 0.5464 Acc: 0.7190\n",
      "has spend time 5m 22s/n\n",
      "\n",
      "Epoch 150/9999\n",
      "----------\n",
      "train Loss: 0.5128 Acc: 0.7459\n",
      "has spend time 5m 24s/n\n",
      "val Loss: 0.5509 Acc: 0.6928\n",
      "has spend time 5m 24s/n\n",
      "\n",
      "Epoch 151/9999\n",
      "----------\n",
      "train Loss: 0.5024 Acc: 0.7459\n",
      "has spend time 5m 26s/n\n",
      "val Loss: 0.5456 Acc: 0.7124\n",
      "has spend time 5m 26s/n\n",
      "\n",
      "Epoch 152/9999\n",
      "----------\n",
      "train Loss: 0.5033 Acc: 0.7664\n",
      "has spend time 5m 28s/n\n",
      "val Loss: 0.5457 Acc: 0.7059\n",
      "has spend time 5m 28s/n\n",
      "\n",
      "Epoch 153/9999\n",
      "----------\n",
      "train Loss: 0.5086 Acc: 0.7705\n",
      "has spend time 5m 30s/n\n",
      "val Loss: 0.5496 Acc: 0.7190\n",
      "has spend time 5m 30s/n\n",
      "\n",
      "Epoch 154/9999\n",
      "----------\n",
      "train Loss: 0.5091 Acc: 0.7254\n",
      "has spend time 5m 32s/n\n",
      "val Loss: 0.5586 Acc: 0.7059\n",
      "has spend time 5m 32s/n\n",
      "\n",
      "Epoch 155/9999\n",
      "----------\n",
      "train Loss: 0.4829 Acc: 0.7787\n",
      "has spend time 5m 34s/n\n",
      "val Loss: 0.5480 Acc: 0.7124\n",
      "has spend time 5m 35s/n\n",
      "\n",
      "Epoch 156/9999\n",
      "----------\n",
      "train Loss: 0.5062 Acc: 0.7254\n",
      "has spend time 5m 36s/n\n",
      "val Loss: 0.5520 Acc: 0.7059\n",
      "has spend time 5m 37s/n\n",
      "\n",
      "Epoch 157/9999\n",
      "----------\n",
      "train Loss: 0.5435 Acc: 0.7500\n",
      "has spend time 5m 38s/n\n",
      "val Loss: 0.5665 Acc: 0.6928\n",
      "has spend time 5m 39s/n\n",
      "\n",
      "Epoch 158/9999\n",
      "----------\n",
      "train Loss: 0.5369 Acc: 0.6926\n",
      "has spend time 5m 40s/n\n",
      "val Loss: 0.5672 Acc: 0.6863\n",
      "has spend time 5m 41s/n\n",
      "\n",
      "Epoch 159/9999\n",
      "----------\n",
      "train Loss: 0.5135 Acc: 0.7172\n",
      "has spend time 5m 42s/n\n",
      "val Loss: 0.5439 Acc: 0.7059\n",
      "has spend time 5m 43s/n\n",
      "\n",
      "Epoch 160/9999\n",
      "----------\n",
      "train Loss: 0.5023 Acc: 0.7459\n",
      "has spend time 5m 44s/n\n",
      "val Loss: 0.5476 Acc: 0.7124\n",
      "has spend time 5m 45s/n\n",
      "\n",
      "Epoch 161/9999\n",
      "----------\n",
      "train Loss: 0.5087 Acc: 0.7459\n",
      "has spend time 5m 47s/n\n",
      "val Loss: 0.5465 Acc: 0.6928\n",
      "has spend time 5m 47s/n\n",
      "\n",
      "Epoch 162/9999\n",
      "----------\n",
      "train Loss: 0.5095 Acc: 0.7295\n",
      "has spend time 5m 49s/n\n",
      "val Loss: 0.5422 Acc: 0.7255\n",
      "has spend time 5m 49s/n\n",
      "\n",
      "Epoch 163/9999\n",
      "----------\n",
      "train Loss: 0.5181 Acc: 0.6926\n",
      "has spend time 5m 51s/n\n",
      "val Loss: 0.5479 Acc: 0.7059\n",
      "has spend time 5m 51s/n\n",
      "\n",
      "Epoch 164/9999\n",
      "----------\n",
      "train Loss: 0.4953 Acc: 0.7541\n",
      "has spend time 5m 53s/n\n",
      "val Loss: 0.5506 Acc: 0.6993\n",
      "has spend time 5m 53s/n\n",
      "\n",
      "Epoch 165/9999\n",
      "----------\n",
      "train Loss: 0.5016 Acc: 0.7377\n",
      "has spend time 5m 55s/n\n",
      "val Loss: 0.5665 Acc: 0.6993\n",
      "has spend time 5m 55s/n\n",
      "\n",
      "Epoch 166/9999\n",
      "----------\n",
      "train Loss: 0.5113 Acc: 0.7336\n",
      "has spend time 5m 57s/n\n",
      "val Loss: 0.5457 Acc: 0.7124\n",
      "has spend time 5m 57s/n\n",
      "\n",
      "Epoch 167/9999\n",
      "----------\n",
      "train Loss: 0.5237 Acc: 0.7295\n",
      "has spend time 5m 59s/n\n",
      "val Loss: 0.5459 Acc: 0.7255\n",
      "has spend time 5m 59s/n\n",
      "\n",
      "Epoch 168/9999\n",
      "----------\n",
      "train Loss: 0.4893 Acc: 0.7377\n",
      "has spend time 6m 1s/n\n",
      "val Loss: 0.5498 Acc: 0.7124\n",
      "has spend time 6m 1s/n\n",
      "\n",
      "Epoch 169/9999\n",
      "----------\n",
      "train Loss: 0.4963 Acc: 0.7623\n",
      "has spend time 6m 3s/n\n",
      "val Loss: 0.5651 Acc: 0.6863\n",
      "has spend time 6m 4s/n\n",
      "\n",
      "Epoch 170/9999\n",
      "----------\n",
      "train Loss: 0.4911 Acc: 0.7500\n",
      "has spend time 6m 5s/n\n",
      "val Loss: 0.5573 Acc: 0.6993\n",
      "has spend time 6m 6s/n\n",
      "\n",
      "Epoch 171/9999\n",
      "----------\n",
      "train Loss: 0.5205 Acc: 0.7295\n",
      "has spend time 6m 8s/n\n",
      "val Loss: 0.5595 Acc: 0.7059\n",
      "has spend time 6m 8s/n\n",
      "\n",
      "Epoch 172/9999\n",
      "----------\n",
      "train Loss: 0.5295 Acc: 0.7131\n",
      "has spend time 6m 10s/n\n",
      "val Loss: 0.5493 Acc: 0.7190\n",
      "has spend time 6m 10s/n\n",
      "\n",
      "Epoch 173/9999\n",
      "----------\n",
      "train Loss: 0.4854 Acc: 0.7787\n",
      "has spend time 6m 12s/n\n",
      "val Loss: 0.5473 Acc: 0.7124\n",
      "has spend time 6m 12s/n\n",
      "\n",
      "Epoch 174/9999\n",
      "----------\n",
      "train Loss: 0.5090 Acc: 0.7459\n",
      "has spend time 6m 14s/n\n",
      "val Loss: 0.5578 Acc: 0.7124\n",
      "has spend time 6m 15s/n\n",
      "\n",
      "Epoch 175/9999\n",
      "----------\n",
      "train Loss: 0.4990 Acc: 0.7377\n",
      "has spend time 6m 16s/n\n",
      "val Loss: 0.5489 Acc: 0.7124\n",
      "has spend time 6m 17s/n\n",
      "\n",
      "Epoch 176/9999\n",
      "----------\n",
      "train Loss: 0.5005 Acc: 0.7664\n",
      "has spend time 6m 19s/n\n",
      "val Loss: 0.5421 Acc: 0.7190\n",
      "has spend time 6m 19s/n\n",
      "\n",
      "Epoch 177/9999\n",
      "----------\n",
      "train Loss: 0.4896 Acc: 0.7377\n",
      "has spend time 6m 21s/n\n",
      "val Loss: 0.5383 Acc: 0.7190\n",
      "has spend time 6m 22s/n\n",
      "\n",
      "Epoch 178/9999\n",
      "----------\n",
      "train Loss: 0.5110 Acc: 0.7213\n",
      "has spend time 6m 23s/n\n",
      "val Loss: 0.5479 Acc: 0.6993\n",
      "has spend time 6m 24s/n\n",
      "\n",
      "Epoch 179/9999\n",
      "----------\n",
      "train Loss: 0.5026 Acc: 0.7500\n",
      "has spend time 6m 25s/n\n",
      "val Loss: 0.5546 Acc: 0.7059\n",
      "has spend time 6m 26s/n\n",
      "\n",
      "Epoch 180/9999\n",
      "----------\n",
      "train Loss: 0.5307 Acc: 0.7336\n",
      "has spend time 6m 27s/n\n",
      "val Loss: 0.5450 Acc: 0.7124\n",
      "has spend time 6m 28s/n\n",
      "\n",
      "Epoch 181/9999\n",
      "----------\n",
      "train Loss: 0.5112 Acc: 0.7049\n",
      "has spend time 6m 29s/n\n",
      "val Loss: 0.5600 Acc: 0.6928\n",
      "has spend time 6m 30s/n\n",
      "\n",
      "Epoch 182/9999\n",
      "----------\n",
      "train Loss: 0.4831 Acc: 0.7787\n",
      "has spend time 6m 32s/n\n",
      "val Loss: 0.5557 Acc: 0.6863\n",
      "has spend time 6m 32s/n\n",
      "\n",
      "Epoch 183/9999\n",
      "----------\n",
      "train Loss: 0.5047 Acc: 0.7541\n",
      "has spend time 6m 34s/n\n",
      "val Loss: 0.5457 Acc: 0.7190\n",
      "has spend time 6m 34s/n\n",
      "\n",
      "Epoch 184/9999\n",
      "----------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train Loss: 0.5026 Acc: 0.7295\n",
      "has spend time 6m 36s/n\n",
      "val Loss: 0.5433 Acc: 0.7124\n",
      "has spend time 6m 36s/n\n",
      "\n",
      "Epoch 185/9999\n",
      "----------\n",
      "train Loss: 0.4875 Acc: 0.7500\n",
      "has spend time 6m 38s/n\n",
      "val Loss: 0.5461 Acc: 0.7124\n",
      "has spend time 6m 38s/n\n",
      "\n",
      "Epoch 186/9999\n",
      "----------\n",
      "train Loss: 0.5256 Acc: 0.7541\n",
      "has spend time 6m 40s/n\n",
      "val Loss: 0.5566 Acc: 0.7059\n",
      "has spend time 6m 41s/n\n",
      "\n",
      "Epoch 187/9999\n",
      "----------\n",
      "train Loss: 0.4847 Acc: 0.7705\n",
      "has spend time 6m 42s/n\n",
      "val Loss: 0.5668 Acc: 0.6993\n",
      "has spend time 6m 43s/n\n",
      "\n",
      "Epoch 188/9999\n",
      "----------\n",
      "train Loss: 0.5034 Acc: 0.7254\n",
      "has spend time 6m 44s/n\n",
      "val Loss: 0.5821 Acc: 0.6863\n",
      "has spend time 6m 45s/n\n",
      "\n",
      "Epoch 189/9999\n",
      "----------\n",
      "train Loss: 0.5042 Acc: 0.7336\n",
      "has spend time 6m 47s/n\n",
      "val Loss: 0.5577 Acc: 0.6993\n",
      "has spend time 6m 47s/n\n",
      "\n",
      "Epoch 190/9999\n",
      "----------\n",
      "train Loss: 0.5040 Acc: 0.7418\n",
      "has spend time 6m 49s/n\n",
      "val Loss: 0.5427 Acc: 0.7124\n",
      "has spend time 6m 49s/n\n",
      "\n",
      "Epoch 191/9999\n",
      "----------\n",
      "train Loss: 0.5553 Acc: 0.7008\n",
      "has spend time 6m 51s/n\n",
      "val Loss: 0.5504 Acc: 0.7124\n",
      "has spend time 6m 51s/n\n",
      "\n",
      "Epoch 192/9999\n",
      "----------\n",
      "train Loss: 0.4926 Acc: 0.7418\n",
      "has spend time 6m 53s/n\n",
      "val Loss: 0.5578 Acc: 0.7059\n",
      "has spend time 6m 53s/n\n",
      "\n",
      "Epoch 193/9999\n",
      "----------\n",
      "train Loss: 0.5180 Acc: 0.7172\n",
      "has spend time 6m 55s/n\n",
      "val Loss: 0.5517 Acc: 0.7059\n",
      "has spend time 6m 55s/n\n",
      "\n",
      "Epoch 194/9999\n",
      "----------\n",
      "train Loss: 0.5076 Acc: 0.7500\n",
      "has spend time 6m 57s/n\n",
      "val Loss: 0.5573 Acc: 0.7124\n",
      "has spend time 6m 58s/n\n",
      "\n",
      "Epoch 195/9999\n",
      "----------\n",
      "train Loss: 0.5031 Acc: 0.7459\n",
      "has spend time 6m 59s/n\n",
      "val Loss: 0.5525 Acc: 0.6993\n",
      "has spend time 6m 60s/n\n",
      "\n",
      "Epoch 196/9999\n",
      "----------\n",
      "train Loss: 0.5044 Acc: 0.7254\n",
      "has spend time 7m 1s/n\n",
      "val Loss: 0.5413 Acc: 0.7124\n",
      "has spend time 7m 2s/n\n",
      "\n",
      "Epoch 197/9999\n",
      "----------\n",
      "train Loss: 0.5335 Acc: 0.7049\n",
      "has spend time 7m 4s/n\n",
      "val Loss: 0.5503 Acc: 0.6993\n",
      "has spend time 7m 4s/n\n",
      "\n",
      "Epoch 198/9999\n",
      "----------\n",
      "train Loss: 0.5016 Acc: 0.7418\n",
      "has spend time 7m 6s/n\n",
      "val Loss: 0.5472 Acc: 0.7124\n",
      "has spend time 7m 6s/n\n",
      "\n",
      "Epoch 199/9999\n",
      "----------\n",
      "train Loss: 0.5147 Acc: 0.7500\n",
      "has spend time 7m 8s/n\n",
      "val Loss: 0.5511 Acc: 0.6993\n",
      "has spend time 7m 8s/n\n",
      "\n",
      "Epoch 200/9999\n",
      "----------\n",
      "train Loss: 0.4820 Acc: 0.7336\n",
      "has spend time 7m 10s/n\n",
      "val Loss: 0.5467 Acc: 0.6993\n",
      "has spend time 7m 10s/n\n",
      "\n",
      "Epoch 201/9999\n",
      "----------\n",
      "train Loss: 0.5098 Acc: 0.7500\n",
      "has spend time 7m 12s/n\n",
      "val Loss: 0.5452 Acc: 0.7124\n",
      "has spend time 7m 13s/n\n",
      "\n",
      "Epoch 202/9999\n",
      "----------\n",
      "train Loss: 0.4897 Acc: 0.7377\n",
      "has spend time 7m 14s/n\n",
      "val Loss: 0.5406 Acc: 0.7190\n",
      "has spend time 7m 15s/n\n",
      "\n",
      "Epoch 203/9999\n",
      "----------\n",
      "train Loss: 0.4843 Acc: 0.7582\n",
      "has spend time 7m 16s/n\n",
      "val Loss: 0.5753 Acc: 0.6928\n",
      "has spend time 7m 17s/n\n",
      "\n",
      "Epoch 204/9999\n",
      "----------\n",
      "train Loss: 0.5119 Acc: 0.7582\n",
      "has spend time 7m 18s/n\n",
      "val Loss: 0.5605 Acc: 0.6928\n",
      "has spend time 7m 19s/n\n",
      "\n",
      "Epoch 205/9999\n",
      "----------\n",
      "train Loss: 0.5148 Acc: 0.7295\n",
      "has spend time 7m 20s/n\n",
      "val Loss: 0.5504 Acc: 0.7124\n",
      "has spend time 7m 21s/n\n",
      "\n",
      "Epoch 206/9999\n",
      "----------\n",
      "train Loss: 0.5161 Acc: 0.7418\n",
      "has spend time 7m 22s/n\n",
      "val Loss: 0.5498 Acc: 0.7059\n",
      "has spend time 7m 23s/n\n",
      "\n",
      "Epoch 207/9999\n",
      "----------\n",
      "train Loss: 0.5061 Acc: 0.7541\n",
      "has spend time 7m 25s/n\n",
      "val Loss: 0.5704 Acc: 0.6993\n",
      "has spend time 7m 25s/n\n",
      "\n",
      "Epoch 208/9999\n",
      "----------\n",
      "train Loss: 0.5002 Acc: 0.7295\n",
      "has spend time 7m 27s/n\n",
      "val Loss: 0.5446 Acc: 0.7124\n",
      "has spend time 7m 27s/n\n",
      "\n",
      "Epoch 209/9999\n",
      "----------\n",
      "train Loss: 0.5008 Acc: 0.7500\n",
      "has spend time 7m 29s/n\n",
      "val Loss: 0.5455 Acc: 0.7124\n",
      "has spend time 7m 30s/n\n",
      "\n",
      "Epoch 210/9999\n",
      "----------\n",
      "train Loss: 0.5019 Acc: 0.7254\n",
      "has spend time 7m 31s/n\n",
      "val Loss: 0.5382 Acc: 0.7255\n",
      "has spend time 7m 32s/n\n",
      "\n",
      "Epoch 211/9999\n",
      "----------\n",
      "train Loss: 0.5028 Acc: 0.7623\n",
      "has spend time 7m 33s/n\n",
      "val Loss: 0.5381 Acc: 0.7124\n",
      "has spend time 7m 34s/n\n",
      "\n",
      "Epoch 212/9999\n",
      "----------\n",
      "train Loss: 0.5045 Acc: 0.7500\n",
      "has spend time 7m 35s/n\n",
      "val Loss: 0.5697 Acc: 0.6993\n",
      "has spend time 7m 36s/n\n",
      "\n",
      "Epoch 213/9999\n",
      "----------\n",
      "train Loss: 0.5284 Acc: 0.6967\n",
      "has spend time 7m 37s/n\n",
      "val Loss: 0.5522 Acc: 0.7059\n",
      "has spend time 7m 38s/n\n",
      "\n",
      "Epoch 214/9999\n",
      "----------\n",
      "train Loss: 0.5216 Acc: 0.7500\n",
      "has spend time 7m 40s/n\n",
      "val Loss: 0.5451 Acc: 0.7124\n",
      "has spend time 7m 40s/n\n",
      "\n",
      "Epoch 215/9999\n",
      "----------\n",
      "train Loss: 0.4998 Acc: 0.7541\n",
      "has spend time 7m 42s/n\n",
      "val Loss: 0.5445 Acc: 0.6993\n",
      "has spend time 7m 42s/n\n",
      "\n",
      "Epoch 216/9999\n",
      "----------\n",
      "train Loss: 0.4854 Acc: 0.7623\n",
      "has spend time 7m 44s/n\n",
      "val Loss: 0.5462 Acc: 0.7190\n",
      "has spend time 7m 45s/n\n",
      "\n",
      "Epoch 217/9999\n",
      "----------\n",
      "train Loss: 0.5098 Acc: 0.7008\n",
      "has spend time 7m 46s/n\n",
      "val Loss: 0.5438 Acc: 0.7124\n",
      "has spend time 7m 47s/n\n",
      "\n",
      "Epoch 218/9999\n",
      "----------\n",
      "train Loss: 0.5128 Acc: 0.7377\n",
      "has spend time 7m 48s/n\n",
      "val Loss: 0.5523 Acc: 0.7190\n",
      "has spend time 7m 49s/n\n",
      "\n",
      "Epoch 219/9999\n",
      "----------\n",
      "train Loss: 0.5281 Acc: 0.7213\n",
      "has spend time 7m 51s/n\n",
      "val Loss: 0.5586 Acc: 0.7059\n",
      "has spend time 7m 51s/n\n",
      "\n",
      "Epoch 220/9999\n",
      "----------\n",
      "train Loss: 0.5096 Acc: 0.7172\n",
      "has spend time 7m 53s/n\n",
      "val Loss: 0.5526 Acc: 0.7059\n",
      "has spend time 7m 53s/n\n",
      "\n",
      "Epoch 221/9999\n",
      "----------\n",
      "train Loss: 0.4882 Acc: 0.7418\n",
      "has spend time 7m 55s/n\n",
      "val Loss: 0.5640 Acc: 0.6993\n",
      "has spend time 7m 55s/n\n",
      "\n",
      "Epoch 222/9999\n",
      "----------\n",
      "train Loss: 0.5160 Acc: 0.7295\n",
      "has spend time 7m 57s/n\n",
      "val Loss: 0.5478 Acc: 0.7124\n",
      "has spend time 7m 57s/n\n",
      "\n",
      "Epoch 223/9999\n",
      "----------\n",
      "train Loss: 0.5163 Acc: 0.7336\n",
      "has spend time 7m 59s/n\n",
      "val Loss: 0.5431 Acc: 0.7124\n",
      "has spend time 7m 59s/n\n",
      "\n",
      "Epoch 224/9999\n",
      "----------\n",
      "train Loss: 0.5257 Acc: 0.7377\n",
      "has spend time 8m 1s/n\n",
      "val Loss: 0.5382 Acc: 0.7124\n",
      "has spend time 8m 1s/n\n",
      "\n",
      "Epoch 225/9999\n",
      "----------\n",
      "train Loss: 0.5125 Acc: 0.7541\n",
      "has spend time 8m 3s/n\n",
      "val Loss: 0.5468 Acc: 0.7124\n",
      "has spend time 8m 4s/n\n",
      "\n",
      "Epoch 226/9999\n",
      "----------\n",
      "train Loss: 0.5236 Acc: 0.7500\n",
      "has spend time 8m 5s/n\n",
      "val Loss: 0.5551 Acc: 0.7124\n",
      "has spend time 8m 6s/n\n",
      "\n",
      "Epoch 227/9999\n",
      "----------\n",
      "train Loss: 0.4741 Acc: 0.7500\n",
      "has spend time 8m 8s/n\n",
      "val Loss: 0.5541 Acc: 0.7059\n",
      "has spend time 8m 8s/n\n",
      "\n",
      "Epoch 228/9999\n",
      "----------\n",
      "train Loss: 0.4868 Acc: 0.7746\n",
      "has spend time 8m 10s/n\n",
      "val Loss: 0.5491 Acc: 0.6993\n",
      "has spend time 8m 11s/n\n",
      "\n",
      "Epoch 229/9999\n",
      "----------\n",
      "train Loss: 0.5044 Acc: 0.7377\n",
      "has spend time 8m 12s/n\n",
      "val Loss: 0.5530 Acc: 0.7059\n",
      "has spend time 8m 13s/n\n",
      "\n",
      "Epoch 230/9999\n",
      "----------\n",
      "train Loss: 0.4822 Acc: 0.7459\n",
      "has spend time 8m 15s/n\n",
      "val Loss: 0.5561 Acc: 0.6928\n",
      "has spend time 8m 15s/n\n",
      "\n",
      "Epoch 231/9999\n",
      "----------\n",
      "train Loss: 0.5221 Acc: 0.7295\n",
      "has spend time 8m 17s/n\n",
      "val Loss: 0.5428 Acc: 0.7059\n",
      "has spend time 8m 17s/n\n",
      "\n",
      "Epoch 232/9999\n",
      "----------\n",
      "train Loss: 0.4832 Acc: 0.7623\n",
      "has spend time 8m 19s/n\n",
      "val Loss: 0.5676 Acc: 0.6993\n",
      "has spend time 8m 19s/n\n",
      "\n",
      "Epoch 233/9999\n",
      "----------\n",
      "train Loss: 0.5174 Acc: 0.7213\n",
      "has spend time 8m 21s/n\n",
      "val Loss: 0.5563 Acc: 0.7059\n",
      "has spend time 8m 22s/n\n",
      "\n",
      "Epoch 234/9999\n",
      "----------\n",
      "train Loss: 0.5268 Acc: 0.7049\n",
      "has spend time 8m 23s/n\n",
      "val Loss: 0.5552 Acc: 0.6993\n",
      "has spend time 8m 24s/n\n",
      "\n",
      "Epoch 235/9999\n",
      "----------\n",
      "train Loss: 0.4644 Acc: 0.7541\n",
      "has spend time 8m 25s/n\n",
      "val Loss: 0.5468 Acc: 0.6993\n",
      "has spend time 8m 26s/n\n",
      "\n",
      "Epoch 236/9999\n",
      "----------\n",
      "train Loss: 0.4942 Acc: 0.7418\n",
      "has spend time 8m 27s/n\n",
      "val Loss: 0.5460 Acc: 0.7255\n",
      "has spend time 8m 28s/n\n",
      "\n",
      "Epoch 237/9999\n",
      "----------\n",
      "train Loss: 0.5168 Acc: 0.7213\n",
      "has spend time 8m 29s/n\n",
      "val Loss: 0.5504 Acc: 0.6993\n",
      "has spend time 8m 30s/n\n",
      "\n",
      "Epoch 238/9999\n",
      "----------\n",
      "train Loss: 0.5055 Acc: 0.7336\n",
      "has spend time 8m 31s/n\n",
      "val Loss: 0.5579 Acc: 0.7059\n",
      "has spend time 8m 32s/n\n",
      "\n",
      "Epoch 239/9999\n",
      "----------\n",
      "train Loss: 0.5193 Acc: 0.7418\n",
      "has spend time 8m 34s/n\n",
      "val Loss: 0.5503 Acc: 0.6928\n",
      "has spend time 8m 35s/n\n",
      "\n",
      "Epoch 240/9999\n",
      "----------\n",
      "train Loss: 0.4844 Acc: 0.7418\n",
      "has spend time 8m 36s/n\n",
      "val Loss: 0.5474 Acc: 0.7124\n",
      "has spend time 8m 37s/n\n",
      "\n",
      "Epoch 241/9999\n",
      "----------\n",
      "train Loss: 0.5188 Acc: 0.7418\n",
      "has spend time 8m 38s/n\n",
      "val Loss: 0.5516 Acc: 0.7059\n",
      "has spend time 8m 39s/n\n",
      "\n",
      "Epoch 242/9999\n",
      "----------\n",
      "train Loss: 0.5019 Acc: 0.7254\n",
      "has spend time 8m 40s/n\n",
      "val Loss: 0.5722 Acc: 0.6928\n",
      "has spend time 8m 41s/n\n",
      "\n",
      "Epoch 243/9999\n",
      "----------\n",
      "train Loss: 0.5130 Acc: 0.7459\n",
      "has spend time 8m 42s/n\n",
      "val Loss: 0.5757 Acc: 0.6993\n",
      "has spend time 8m 43s/n\n",
      "\n",
      "Epoch 244/9999\n",
      "----------\n",
      "train Loss: 0.5042 Acc: 0.7377\n",
      "has spend time 8m 44s/n\n",
      "val Loss: 0.5665 Acc: 0.6928\n",
      "has spend time 8m 45s/n\n",
      "\n",
      "Epoch 245/9999\n",
      "----------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train Loss: 0.5155 Acc: 0.7049\n",
      "has spend time 8m 47s/n\n",
      "val Loss: 0.5509 Acc: 0.6928\n",
      "has spend time 8m 47s/n\n",
      "\n",
      "Epoch 246/9999\n",
      "----------\n",
      "train Loss: 0.5064 Acc: 0.7172\n",
      "has spend time 8m 49s/n\n",
      "val Loss: 0.5520 Acc: 0.7124\n",
      "has spend time 8m 49s/n\n",
      "\n",
      "Epoch 247/9999\n",
      "----------\n",
      "train Loss: 0.5152 Acc: 0.7131\n",
      "has spend time 8m 51s/n\n",
      "val Loss: 0.5434 Acc: 0.7124\n",
      "has spend time 8m 51s/n\n",
      "\n",
      "Epoch 248/9999\n",
      "----------\n",
      "train Loss: 0.5104 Acc: 0.7418\n",
      "has spend time 8m 53s/n\n",
      "val Loss: 0.5521 Acc: 0.7059\n",
      "has spend time 8m 54s/n\n",
      "\n",
      "Epoch 249/9999\n",
      "----------\n",
      "train Loss: 0.4868 Acc: 0.7500\n",
      "has spend time 8m 55s/n\n",
      "val Loss: 0.5460 Acc: 0.7124\n",
      "has spend time 8m 56s/n\n",
      "\n",
      "Epoch 250/9999\n",
      "----------\n",
      "train Loss: 0.5059 Acc: 0.7541\n",
      "has spend time 8m 57s/n\n",
      "val Loss: 0.5439 Acc: 0.7255\n",
      "has spend time 8m 58s/n\n",
      "\n",
      "Epoch 251/9999\n",
      "----------\n",
      "train Loss: 0.5201 Acc: 0.7213\n",
      "has spend time 8m 60s/n\n",
      "val Loss: 0.5509 Acc: 0.7059\n",
      "has spend time 9m 1s/n\n",
      "\n",
      "Epoch 252/9999\n",
      "----------\n",
      "train Loss: 0.5176 Acc: 0.7336\n",
      "has spend time 9m 2s/n\n",
      "val Loss: 0.5490 Acc: 0.7059\n",
      "has spend time 9m 3s/n\n",
      "\n",
      "Epoch 253/9999\n",
      "----------\n",
      "train Loss: 0.5267 Acc: 0.7377\n",
      "has spend time 9m 4s/n\n",
      "val Loss: 0.5439 Acc: 0.7124\n",
      "has spend time 9m 5s/n\n",
      "\n",
      "Epoch 254/9999\n",
      "----------\n",
      "train Loss: 0.5290 Acc: 0.7172\n",
      "has spend time 9m 6s/n\n",
      "val Loss: 0.5556 Acc: 0.6928\n",
      "has spend time 9m 7s/n\n",
      "\n",
      "Epoch 255/9999\n",
      "----------\n",
      "train Loss: 0.5238 Acc: 0.7541\n",
      "has spend time 9m 8s/n\n",
      "val Loss: 0.5496 Acc: 0.7124\n",
      "has spend time 9m 9s/n\n",
      "\n",
      "Epoch 256/9999\n",
      "----------\n",
      "train Loss: 0.5342 Acc: 0.7049\n",
      "has spend time 9m 11s/n\n",
      "val Loss: 0.5516 Acc: 0.6993\n",
      "has spend time 9m 11s/n\n",
      "\n",
      "Epoch 257/9999\n",
      "----------\n",
      "train Loss: 0.5127 Acc: 0.7459\n",
      "has spend time 9m 13s/n\n",
      "val Loss: 0.5559 Acc: 0.7059\n",
      "has spend time 9m 13s/n\n",
      "\n",
      "Epoch 258/9999\n",
      "----------\n",
      "train Loss: 0.4956 Acc: 0.7377\n",
      "has spend time 9m 15s/n\n",
      "val Loss: 0.5575 Acc: 0.6993\n",
      "has spend time 9m 15s/n\n",
      "\n",
      "Epoch 259/9999\n",
      "----------\n",
      "train Loss: 0.5383 Acc: 0.7213\n",
      "has spend time 9m 17s/n\n",
      "val Loss: 0.5513 Acc: 0.7190\n",
      "has spend time 9m 17s/n\n",
      "\n",
      "Epoch 260/9999\n",
      "----------\n",
      "train Loss: 0.4825 Acc: 0.7418\n",
      "has spend time 9m 19s/n\n",
      "val Loss: 0.5460 Acc: 0.7059\n",
      "has spend time 9m 20s/n\n",
      "\n",
      "Epoch 261/9999\n",
      "----------\n",
      "train Loss: 0.5519 Acc: 0.6885\n",
      "has spend time 9m 21s/n\n",
      "val Loss: 0.5520 Acc: 0.6993\n",
      "has spend time 9m 22s/n\n",
      "\n",
      "Epoch 262/9999\n",
      "----------\n",
      "train Loss: 0.5219 Acc: 0.7295\n",
      "has spend time 9m 23s/n\n",
      "val Loss: 0.5501 Acc: 0.7124\n",
      "has spend time 9m 24s/n\n",
      "\n",
      "Epoch 263/9999\n",
      "----------\n",
      "train Loss: 0.4884 Acc: 0.7787\n",
      "has spend time 9m 25s/n\n",
      "val Loss: 0.5561 Acc: 0.7124\n",
      "has spend time 9m 26s/n\n",
      "\n",
      "Epoch 264/9999\n",
      "----------\n",
      "train Loss: 0.5120 Acc: 0.7541\n",
      "has spend time 9m 27s/n\n",
      "val Loss: 0.5493 Acc: 0.7190\n",
      "has spend time 9m 28s/n\n",
      "\n",
      "Epoch 265/9999\n",
      "----------\n",
      "train Loss: 0.4944 Acc: 0.7500\n",
      "has spend time 9m 29s/n\n",
      "val Loss: 0.5549 Acc: 0.7124\n",
      "has spend time 9m 30s/n\n",
      "\n",
      "Epoch 266/9999\n",
      "----------\n",
      "train Loss: 0.5116 Acc: 0.7131\n",
      "has spend time 9m 31s/n\n",
      "val Loss: 0.5489 Acc: 0.7320\n",
      "has spend time 9m 32s/n\n",
      "\n",
      "Epoch 267/9999\n",
      "----------\n",
      "train Loss: 0.5219 Acc: 0.7500\n",
      "has spend time 9m 33s/n\n",
      "val Loss: 0.5439 Acc: 0.7190\n",
      "has spend time 9m 34s/n\n",
      "\n",
      "Epoch 268/9999\n",
      "----------\n",
      "train Loss: 0.5212 Acc: 0.7172\n",
      "has spend time 9m 36s/n\n",
      "val Loss: 0.5627 Acc: 0.6928\n",
      "has spend time 9m 36s/n\n",
      "\n",
      "Epoch 269/9999\n",
      "----------\n",
      "train Loss: 0.5454 Acc: 0.7008\n",
      "has spend time 9m 38s/n\n",
      "val Loss: 0.5562 Acc: 0.7124\n",
      "has spend time 9m 39s/n\n",
      "\n",
      "Epoch 270/9999\n",
      "----------\n",
      "train Loss: 0.4985 Acc: 0.7623\n",
      "has spend time 9m 40s/n\n",
      "val Loss: 0.5498 Acc: 0.7059\n",
      "has spend time 9m 41s/n\n",
      "\n",
      "Epoch 271/9999\n",
      "----------\n",
      "train Loss: 0.4995 Acc: 0.7623\n",
      "has spend time 9m 43s/n\n",
      "val Loss: 0.5525 Acc: 0.7190\n",
      "has spend time 9m 43s/n\n",
      "\n",
      "Epoch 272/9999\n",
      "----------\n",
      "train Loss: 0.5190 Acc: 0.7295\n",
      "has spend time 9m 45s/n\n",
      "val Loss: 0.5477 Acc: 0.7059\n",
      "has spend time 9m 46s/n\n",
      "\n",
      "Epoch 273/9999\n",
      "----------\n",
      "train Loss: 0.5173 Acc: 0.7008\n",
      "has spend time 9m 47s/n\n",
      "val Loss: 0.5558 Acc: 0.6993\n",
      "has spend time 9m 48s/n\n",
      "\n",
      "Epoch 274/9999\n",
      "----------\n",
      "train Loss: 0.5132 Acc: 0.7172\n",
      "has spend time 9m 49s/n\n",
      "val Loss: 0.5522 Acc: 0.7124\n",
      "has spend time 9m 50s/n\n",
      "\n",
      "Epoch 275/9999\n",
      "----------\n",
      "train Loss: 0.5015 Acc: 0.7459\n",
      "has spend time 9m 52s/n\n",
      "val Loss: 0.5597 Acc: 0.6928\n",
      "has spend time 9m 52s/n\n",
      "\n",
      "Epoch 276/9999\n",
      "----------\n",
      "train Loss: 0.5280 Acc: 0.7582\n",
      "has spend time 9m 54s/n\n",
      "val Loss: 0.5447 Acc: 0.6993\n",
      "has spend time 9m 54s/n\n",
      "\n",
      "Epoch 277/9999\n",
      "----------\n",
      "train Loss: 0.5063 Acc: 0.7582\n",
      "has spend time 9m 56s/n\n",
      "val Loss: 0.5600 Acc: 0.6928\n",
      "has spend time 9m 56s/n\n",
      "\n",
      "Epoch 278/9999\n",
      "----------\n",
      "train Loss: 0.5354 Acc: 0.7377\n",
      "has spend time 9m 58s/n\n",
      "val Loss: 0.5526 Acc: 0.6928\n",
      "has spend time 9m 58s/n\n",
      "\n",
      "Epoch 279/9999\n",
      "----------\n",
      "train Loss: 0.5081 Acc: 0.7254\n",
      "has spend time 9m 60s/n\n",
      "val Loss: 0.5461 Acc: 0.7124\n",
      "has spend time 10m 1s/n\n",
      "\n",
      "Epoch 280/9999\n",
      "----------\n",
      "train Loss: 0.5146 Acc: 0.7459\n",
      "has spend time 10m 2s/n\n",
      "val Loss: 0.5592 Acc: 0.7059\n",
      "has spend time 10m 3s/n\n",
      "\n",
      "Epoch 281/9999\n",
      "----------\n",
      "train Loss: 0.5079 Acc: 0.7172\n",
      "has spend time 10m 5s/n\n",
      "val Loss: 0.5533 Acc: 0.7190\n",
      "has spend time 10m 5s/n\n",
      "\n",
      "Epoch 282/9999\n",
      "----------\n",
      "train Loss: 0.5649 Acc: 0.6885\n",
      "has spend time 10m 7s/n\n",
      "val Loss: 0.5521 Acc: 0.7124\n",
      "has spend time 10m 8s/n\n",
      "\n",
      "Epoch 283/9999\n",
      "----------\n",
      "train Loss: 0.5188 Acc: 0.7459\n",
      "has spend time 10m 9s/n\n",
      "val Loss: 0.5490 Acc: 0.7059\n",
      "has spend time 10m 10s/n\n",
      "\n",
      "Epoch 284/9999\n",
      "----------\n",
      "train Loss: 0.4947 Acc: 0.7828\n",
      "has spend time 10m 11s/n\n",
      "val Loss: 0.5479 Acc: 0.7059\n",
      "has spend time 10m 12s/n\n",
      "\n",
      "Epoch 285/9999\n",
      "----------\n",
      "train Loss: 0.5157 Acc: 0.7172\n",
      "has spend time 10m 13s/n\n",
      "val Loss: 0.5475 Acc: 0.7124\n",
      "has spend time 10m 14s/n\n",
      "\n",
      "Epoch 286/9999\n",
      "----------\n",
      "train Loss: 0.5095 Acc: 0.7295\n",
      "has spend time 10m 15s/n\n",
      "val Loss: 0.5574 Acc: 0.7124\n",
      "has spend time 10m 16s/n\n",
      "\n",
      "Epoch 287/9999\n",
      "----------\n",
      "train Loss: 0.5156 Acc: 0.7213\n",
      "has spend time 10m 17s/n\n",
      "val Loss: 0.5528 Acc: 0.7059\n",
      "has spend time 10m 18s/n\n",
      "\n",
      "Epoch 288/9999\n",
      "----------\n",
      "train Loss: 0.5241 Acc: 0.7418\n",
      "has spend time 10m 19s/n\n",
      "val Loss: 0.5593 Acc: 0.6993\n",
      "has spend time 10m 20s/n\n",
      "\n",
      "Epoch 289/9999\n",
      "----------\n",
      "train Loss: 0.5409 Acc: 0.7008\n",
      "has spend time 10m 21s/n\n",
      "val Loss: 0.5495 Acc: 0.6993\n",
      "has spend time 10m 22s/n\n",
      "\n",
      "Epoch 290/9999\n",
      "----------\n",
      "train Loss: 0.4953 Acc: 0.7418\n",
      "has spend time 10m 23s/n\n",
      "val Loss: 0.5521 Acc: 0.7059\n",
      "has spend time 10m 24s/n\n",
      "\n",
      "Epoch 291/9999\n",
      "----------\n",
      "train Loss: 0.4992 Acc: 0.7541\n",
      "has spend time 10m 25s/n\n",
      "val Loss: 0.5519 Acc: 0.7190\n",
      "has spend time 10m 26s/n\n",
      "\n",
      "Epoch 292/9999\n",
      "----------\n",
      "train Loss: 0.5121 Acc: 0.7418\n",
      "has spend time 10m 28s/n\n",
      "val Loss: 0.5493 Acc: 0.7124\n",
      "has spend time 10m 28s/n\n",
      "\n",
      "Epoch 293/9999\n",
      "----------\n",
      "train Loss: 0.4915 Acc: 0.7500\n",
      "has spend time 10m 30s/n\n",
      "val Loss: 0.5562 Acc: 0.6863\n",
      "has spend time 10m 31s/n\n",
      "\n",
      "Epoch 294/9999\n",
      "----------\n",
      "train Loss: 0.5116 Acc: 0.7541\n",
      "has spend time 10m 32s/n\n",
      "val Loss: 0.5535 Acc: 0.6928\n",
      "has spend time 10m 33s/n\n",
      "\n",
      "Epoch 295/9999\n",
      "----------\n",
      "train Loss: 0.4946 Acc: 0.7336\n",
      "has spend time 10m 34s/n\n",
      "val Loss: 0.5605 Acc: 0.7124\n",
      "has spend time 10m 35s/n\n",
      "\n",
      "Epoch 296/9999\n",
      "----------\n",
      "train Loss: 0.4978 Acc: 0.7418\n",
      "has spend time 10m 36s/n\n",
      "val Loss: 0.5586 Acc: 0.6928\n",
      "has spend time 10m 37s/n\n",
      "\n",
      "Epoch 297/9999\n",
      "----------\n",
      "train Loss: 0.5267 Acc: 0.7213\n",
      "has spend time 10m 38s/n\n",
      "val Loss: 0.5489 Acc: 0.6993\n",
      "has spend time 10m 39s/n\n",
      "\n",
      "Epoch 298/9999\n",
      "----------\n",
      "train Loss: 0.5160 Acc: 0.6885\n",
      "has spend time 10m 40s/n\n",
      "val Loss: 0.5497 Acc: 0.7059\n",
      "has spend time 10m 41s/n\n",
      "\n",
      "Epoch 299/9999\n",
      "----------\n",
      "train Loss: 0.4812 Acc: 0.7582\n",
      "has spend time 10m 43s/n\n",
      "val Loss: 0.5471 Acc: 0.6993\n",
      "has spend time 10m 44s/n\n",
      "\n",
      "Epoch 300/9999\n",
      "----------\n",
      "train Loss: 0.5358 Acc: 0.7008\n",
      "has spend time 10m 45s/n\n",
      "val Loss: 0.5512 Acc: 0.7059\n",
      "has spend time 10m 46s/n\n",
      "\n",
      "Epoch 301/9999\n",
      "----------\n",
      "train Loss: 0.5043 Acc: 0.7418\n",
      "has spend time 10m 47s/n\n",
      "val Loss: 0.5427 Acc: 0.7255\n",
      "has spend time 10m 48s/n\n",
      "\n",
      "Epoch 302/9999\n",
      "----------\n",
      "train Loss: 0.5530 Acc: 0.7131\n",
      "has spend time 10m 49s/n\n",
      "val Loss: 0.5446 Acc: 0.7059\n",
      "has spend time 10m 50s/n\n",
      "\n",
      "Epoch 303/9999\n",
      "----------\n",
      "train Loss: 0.5133 Acc: 0.7582\n",
      "has spend time 10m 51s/n\n",
      "val Loss: 0.5562 Acc: 0.7059\n",
      "has spend time 10m 52s/n\n",
      "\n",
      "Epoch 304/9999\n",
      "----------\n",
      "train Loss: 0.5235 Acc: 0.7459\n",
      "has spend time 10m 53s/n\n",
      "val Loss: 0.5557 Acc: 0.7059\n",
      "has spend time 10m 54s/n\n",
      "\n",
      "Epoch 305/9999\n",
      "----------\n",
      "train Loss: 0.5065 Acc: 0.7869\n",
      "has spend time 10m 55s/n\n",
      "val Loss: 0.5722 Acc: 0.6928\n",
      "has spend time 10m 56s/n\n",
      "\n",
      "Epoch 306/9999\n",
      "----------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train Loss: 0.4997 Acc: 0.7459\n",
      "has spend time 10m 57s/n\n",
      "val Loss: 0.5505 Acc: 0.7059\n",
      "has spend time 10m 58s/n\n",
      "\n",
      "Epoch 307/9999\n",
      "----------\n",
      "train Loss: 0.5005 Acc: 0.7131\n",
      "has spend time 10m 60s/n\n",
      "val Loss: 0.5577 Acc: 0.6928\n",
      "has spend time 11m 0s/n\n",
      "\n",
      "Epoch 308/9999\n",
      "----------\n",
      "train Loss: 0.4868 Acc: 0.7213\n",
      "has spend time 11m 2s/n\n",
      "val Loss: 0.5644 Acc: 0.6928\n",
      "has spend time 11m 2s/n\n",
      "\n",
      "Epoch 309/9999\n",
      "----------\n",
      "train Loss: 0.5378 Acc: 0.7131\n",
      "has spend time 11m 4s/n\n",
      "val Loss: 0.5540 Acc: 0.7059\n",
      "has spend time 11m 4s/n\n",
      "\n",
      "Epoch 310/9999\n",
      "----------\n",
      "train Loss: 0.5023 Acc: 0.7172\n",
      "has spend time 11m 6s/n\n",
      "val Loss: 0.5439 Acc: 0.7190\n",
      "has spend time 11m 6s/n\n",
      "\n",
      "Epoch 311/9999\n",
      "----------\n",
      "train Loss: 0.4857 Acc: 0.7377\n",
      "has spend time 11m 8s/n\n",
      "val Loss: 0.5496 Acc: 0.7059\n",
      "has spend time 11m 9s/n\n",
      "\n",
      "Epoch 312/9999\n",
      "----------\n",
      "train Loss: 0.5129 Acc: 0.7172\n",
      "has spend time 11m 10s/n\n",
      "val Loss: 0.5448 Acc: 0.7124\n",
      "has spend time 11m 11s/n\n",
      "\n",
      "Epoch 313/9999\n",
      "----------\n",
      "train Loss: 0.4863 Acc: 0.7500\n",
      "has spend time 11m 13s/n\n",
      "val Loss: 0.5513 Acc: 0.7190\n",
      "has spend time 11m 13s/n\n",
      "\n",
      "Epoch 314/9999\n",
      "----------\n",
      "train Loss: 0.5310 Acc: 0.7213\n",
      "has spend time 11m 15s/n\n",
      "val Loss: 0.5510 Acc: 0.6928\n",
      "has spend time 11m 15s/n\n",
      "\n",
      "Epoch 315/9999\n",
      "----------\n",
      "train Loss: 0.4904 Acc: 0.7336\n",
      "has spend time 11m 17s/n\n",
      "val Loss: 0.5478 Acc: 0.7059\n",
      "has spend time 11m 17s/n\n",
      "\n",
      "Epoch 316/9999\n",
      "----------\n",
      "train Loss: 0.5192 Acc: 0.7172\n",
      "has spend time 11m 19s/n\n",
      "val Loss: 0.5530 Acc: 0.7059\n",
      "has spend time 11m 19s/n\n",
      "\n",
      "Epoch 317/9999\n",
      "----------\n",
      "train Loss: 0.4828 Acc: 0.7705\n",
      "has spend time 11m 21s/n\n",
      "val Loss: 0.5667 Acc: 0.7059\n",
      "has spend time 11m 21s/n\n",
      "\n",
      "Epoch 318/9999\n",
      "----------\n",
      "train Loss: 0.5251 Acc: 0.7172\n",
      "has spend time 11m 23s/n\n",
      "val Loss: 0.5456 Acc: 0.7190\n",
      "has spend time 11m 24s/n\n",
      "\n",
      "Epoch 319/9999\n",
      "----------\n",
      "train Loss: 0.4827 Acc: 0.7828\n",
      "has spend time 11m 25s/n\n",
      "val Loss: 0.5622 Acc: 0.6928\n",
      "has spend time 11m 26s/n\n",
      "\n",
      "Epoch 320/9999\n",
      "----------\n",
      "train Loss: 0.4779 Acc: 0.7500\n",
      "has spend time 11m 28s/n\n",
      "val Loss: 0.5643 Acc: 0.7059\n",
      "has spend time 11m 28s/n\n",
      "\n",
      "Epoch 321/9999\n",
      "----------\n",
      "train Loss: 0.4869 Acc: 0.7541\n",
      "has spend time 11m 30s/n\n",
      "val Loss: 0.5512 Acc: 0.7124\n",
      "has spend time 11m 30s/n\n",
      "\n",
      "Epoch 322/9999\n",
      "----------\n",
      "train Loss: 0.5176 Acc: 0.7459\n",
      "has spend time 11m 32s/n\n",
      "val Loss: 0.5606 Acc: 0.7059\n",
      "has spend time 11m 32s/n\n",
      "\n",
      "Epoch 323/9999\n",
      "----------\n",
      "train Loss: 0.5311 Acc: 0.7049\n",
      "has spend time 11m 34s/n\n",
      "val Loss: 0.5675 Acc: 0.6993\n",
      "has spend time 11m 34s/n\n",
      "\n",
      "Epoch 324/9999\n",
      "----------\n",
      "train Loss: 0.4804 Acc: 0.7541\n",
      "has spend time 11m 36s/n\n",
      "val Loss: 0.5473 Acc: 0.7255\n",
      "has spend time 11m 37s/n\n",
      "\n",
      "Epoch 325/9999\n",
      "----------\n",
      "train Loss: 0.4971 Acc: 0.7541\n",
      "has spend time 11m 38s/n\n",
      "val Loss: 0.5543 Acc: 0.7124\n",
      "has spend time 11m 39s/n\n",
      "\n",
      "Epoch 326/9999\n",
      "----------\n",
      "train Loss: 0.4929 Acc: 0.7705\n",
      "has spend time 11m 41s/n\n",
      "val Loss: 0.5496 Acc: 0.7124\n",
      "has spend time 11m 41s/n\n",
      "\n",
      "Epoch 327/9999\n",
      "----------\n",
      "train Loss: 0.5233 Acc: 0.7377\n",
      "has spend time 11m 43s/n\n",
      "val Loss: 0.5547 Acc: 0.7124\n",
      "has spend time 11m 44s/n\n",
      "\n",
      "Epoch 328/9999\n",
      "----------\n",
      "train Loss: 0.4993 Acc: 0.7336\n",
      "has spend time 11m 45s/n\n",
      "val Loss: 0.5473 Acc: 0.7190\n",
      "has spend time 11m 46s/n\n",
      "\n",
      "Epoch 329/9999\n",
      "----------\n",
      "train Loss: 0.4931 Acc: 0.7541\n",
      "has spend time 11m 47s/n\n",
      "val Loss: 0.5505 Acc: 0.6993\n",
      "has spend time 11m 48s/n\n",
      "\n",
      "Epoch 330/9999\n",
      "----------\n",
      "train Loss: 0.5382 Acc: 0.7049\n",
      "has spend time 11m 49s/n\n",
      "val Loss: 0.5643 Acc: 0.6993\n",
      "has spend time 11m 50s/n\n",
      "\n",
      "Epoch 331/9999\n",
      "----------\n",
      "train Loss: 0.5023 Acc: 0.7295\n",
      "has spend time 11m 52s/n\n",
      "val Loss: 0.5570 Acc: 0.7124\n",
      "has spend time 11m 52s/n\n",
      "\n",
      "Epoch 332/9999\n",
      "----------\n",
      "train Loss: 0.5227 Acc: 0.7254\n",
      "has spend time 11m 54s/n\n",
      "val Loss: 0.5463 Acc: 0.7124\n",
      "has spend time 11m 54s/n\n",
      "\n",
      "Epoch 333/9999\n",
      "----------\n",
      "train Loss: 0.4865 Acc: 0.7623\n",
      "has spend time 11m 56s/n\n",
      "val Loss: 0.5480 Acc: 0.7124\n",
      "has spend time 11m 56s/n\n",
      "\n",
      "Epoch 334/9999\n",
      "----------\n",
      "train Loss: 0.5243 Acc: 0.7008\n",
      "has spend time 11m 58s/n\n",
      "val Loss: 0.5489 Acc: 0.6993\n",
      "has spend time 11m 58s/n\n",
      "\n",
      "Epoch 335/9999\n",
      "----------\n",
      "train Loss: 0.5016 Acc: 0.7664\n",
      "has spend time 11m 60s/n\n",
      "val Loss: 0.5544 Acc: 0.6928\n",
      "has spend time 12m 0s/n\n",
      "\n",
      "Epoch 336/9999\n",
      "----------\n",
      "train Loss: 0.5143 Acc: 0.7008\n",
      "has spend time 12m 2s/n\n",
      "val Loss: 0.5600 Acc: 0.6993\n",
      "has spend time 12m 2s/n\n",
      "\n",
      "Epoch 337/9999\n",
      "----------\n",
      "train Loss: 0.5639 Acc: 0.7049\n",
      "has spend time 12m 4s/n\n",
      "val Loss: 0.5618 Acc: 0.6993\n",
      "has spend time 12m 5s/n\n",
      "\n",
      "Epoch 338/9999\n",
      "----------\n",
      "train Loss: 0.4606 Acc: 0.7869\n",
      "has spend time 12m 6s/n\n",
      "val Loss: 0.5486 Acc: 0.6928\n",
      "has spend time 12m 7s/n\n",
      "\n",
      "Epoch 339/9999\n",
      "----------\n",
      "train Loss: 0.4947 Acc: 0.7377\n",
      "has spend time 12m 8s/n\n",
      "val Loss: 0.5507 Acc: 0.7124\n",
      "has spend time 12m 9s/n\n",
      "\n",
      "Epoch 340/9999\n",
      "----------\n",
      "train Loss: 0.5130 Acc: 0.7418\n",
      "has spend time 12m 11s/n\n",
      "val Loss: 0.5516 Acc: 0.7059\n",
      "has spend time 12m 11s/n\n",
      "\n",
      "Epoch 341/9999\n",
      "----------\n",
      "train Loss: 0.4902 Acc: 0.7500\n",
      "has spend time 12m 13s/n\n",
      "val Loss: 0.5459 Acc: 0.7190\n",
      "has spend time 12m 13s/n\n",
      "\n",
      "Epoch 342/9999\n",
      "----------\n",
      "train Loss: 0.4847 Acc: 0.7541\n",
      "has spend time 12m 15s/n\n",
      "val Loss: 0.5409 Acc: 0.7124\n",
      "has spend time 12m 16s/n\n",
      "\n",
      "Epoch 343/9999\n",
      "----------\n",
      "train Loss: 0.5221 Acc: 0.7459\n",
      "has spend time 12m 17s/n\n",
      "val Loss: 0.5526 Acc: 0.7190\n",
      "has spend time 12m 18s/n\n",
      "\n",
      "Epoch 344/9999\n",
      "----------\n",
      "train Loss: 0.5517 Acc: 0.6680\n",
      "has spend time 12m 20s/n\n",
      "val Loss: 0.5488 Acc: 0.7124\n",
      "has spend time 12m 20s/n\n",
      "\n",
      "Epoch 345/9999\n",
      "----------\n",
      "train Loss: 0.5260 Acc: 0.7172\n",
      "has spend time 12m 22s/n\n",
      "val Loss: 0.5383 Acc: 0.7190\n",
      "has spend time 12m 22s/n\n",
      "\n",
      "Epoch 346/9999\n",
      "----------\n",
      "train Loss: 0.5179 Acc: 0.7254\n",
      "has spend time 12m 24s/n\n",
      "val Loss: 0.5421 Acc: 0.7320\n",
      "has spend time 12m 24s/n\n",
      "\n",
      "Epoch 347/9999\n",
      "----------\n",
      "train Loss: 0.4952 Acc: 0.7377\n",
      "has spend time 12m 26s/n\n",
      "val Loss: 0.5411 Acc: 0.7124\n",
      "has spend time 12m 26s/n\n",
      "\n",
      "Epoch 348/9999\n",
      "----------\n",
      "train Loss: 0.5039 Acc: 0.7377\n",
      "has spend time 12m 28s/n\n",
      "val Loss: 0.5445 Acc: 0.7190\n",
      "has spend time 12m 28s/n\n",
      "\n",
      "Epoch 349/9999\n",
      "----------\n",
      "train Loss: 0.5344 Acc: 0.7295\n",
      "has spend time 12m 30s/n\n",
      "val Loss: 0.5591 Acc: 0.6993\n",
      "has spend time 12m 30s/n\n",
      "\n",
      "Epoch 350/9999\n",
      "----------\n",
      "train Loss: 0.5021 Acc: 0.7582\n",
      "has spend time 12m 32s/n\n",
      "val Loss: 0.5477 Acc: 0.7124\n",
      "has spend time 12m 32s/n\n",
      "\n",
      "Epoch 351/9999\n",
      "----------\n",
      "train Loss: 0.4918 Acc: 0.7336\n",
      "has spend time 12m 34s/n\n",
      "val Loss: 0.5664 Acc: 0.6797\n",
      "has spend time 12m 34s/n\n",
      "\n",
      "Epoch 352/9999\n",
      "----------\n",
      "train Loss: 0.5051 Acc: 0.7213\n",
      "has spend time 12m 36s/n\n",
      "val Loss: 0.5513 Acc: 0.6993\n",
      "has spend time 12m 36s/n\n",
      "\n",
      "Epoch 353/9999\n",
      "----------\n",
      "train Loss: 0.5164 Acc: 0.7295\n",
      "has spend time 12m 38s/n\n",
      "val Loss: 0.5471 Acc: 0.7255\n",
      "has spend time 12m 39s/n\n",
      "\n",
      "Epoch 354/9999\n",
      "----------\n",
      "train Loss: 0.5058 Acc: 0.7254\n",
      "has spend time 12m 40s/n\n",
      "val Loss: 0.5494 Acc: 0.7124\n",
      "has spend time 12m 41s/n\n",
      "\n",
      "Epoch 355/9999\n",
      "----------\n",
      "train Loss: 0.5081 Acc: 0.7295\n",
      "has spend time 12m 42s/n\n",
      "val Loss: 0.5532 Acc: 0.7124\n",
      "has spend time 12m 43s/n\n",
      "\n",
      "Epoch 356/9999\n",
      "----------\n",
      "train Loss: 0.4994 Acc: 0.7418\n",
      "has spend time 12m 44s/n\n",
      "val Loss: 0.5483 Acc: 0.7059\n",
      "has spend time 12m 45s/n\n",
      "\n",
      "Epoch 357/9999\n",
      "----------\n",
      "train Loss: 0.4693 Acc: 0.7623\n",
      "has spend time 12m 46s/n\n",
      "val Loss: 0.5667 Acc: 0.7059\n",
      "has spend time 12m 47s/n\n",
      "\n",
      "Epoch 358/9999\n",
      "----------\n",
      "train Loss: 0.5268 Acc: 0.7336\n",
      "has spend time 12m 48s/n\n",
      "val Loss: 0.5714 Acc: 0.6928\n",
      "has spend time 12m 49s/n\n",
      "\n",
      "Epoch 359/9999\n",
      "----------\n",
      "train Loss: 0.4687 Acc: 0.7541\n",
      "has spend time 12m 50s/n\n",
      "val Loss: 0.5498 Acc: 0.7059\n",
      "has spend time 12m 51s/n\n",
      "\n",
      "Epoch 360/9999\n",
      "----------\n",
      "train Loss: 0.4974 Acc: 0.7582\n",
      "has spend time 12m 53s/n\n",
      "val Loss: 0.5427 Acc: 0.7190\n",
      "has spend time 12m 54s/n\n",
      "\n",
      "Epoch 361/9999\n",
      "----------\n",
      "train Loss: 0.4777 Acc: 0.7623\n",
      "has spend time 12m 55s/n\n",
      "val Loss: 0.5525 Acc: 0.7059\n",
      "has spend time 12m 56s/n\n",
      "\n",
      "Epoch 362/9999\n",
      "----------\n",
      "train Loss: 0.5323 Acc: 0.7213\n",
      "has spend time 12m 57s/n\n",
      "val Loss: 0.5488 Acc: 0.6993\n",
      "has spend time 12m 58s/n\n",
      "\n",
      "Epoch 363/9999\n",
      "----------\n",
      "train Loss: 0.5259 Acc: 0.7049\n",
      "has spend time 12m 59s/n\n",
      "val Loss: 0.5421 Acc: 0.7124\n",
      "has spend time 12m 60s/n\n",
      "\n",
      "Epoch 364/9999\n",
      "----------\n",
      "train Loss: 0.5283 Acc: 0.7008\n",
      "has spend time 13m 1s/n\n",
      "val Loss: 0.5468 Acc: 0.7059\n",
      "has spend time 13m 2s/n\n",
      "\n",
      "Epoch 365/9999\n",
      "----------\n",
      "train Loss: 0.4752 Acc: 0.7705\n",
      "has spend time 13m 4s/n\n",
      "val Loss: 0.5518 Acc: 0.7059\n",
      "has spend time 13m 4s/n\n",
      "\n",
      "Epoch 366/9999\n",
      "----------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train Loss: 0.4868 Acc: 0.7254\n",
      "has spend time 13m 6s/n\n",
      "val Loss: 0.5416 Acc: 0.7190\n",
      "has spend time 13m 6s/n\n",
      "\n",
      "Epoch 367/9999\n",
      "----------\n",
      "train Loss: 0.4806 Acc: 0.7582\n",
      "has spend time 13m 8s/n\n",
      "val Loss: 0.5481 Acc: 0.7124\n",
      "has spend time 13m 9s/n\n",
      "\n",
      "Epoch 368/9999\n",
      "----------\n",
      "train Loss: 0.5123 Acc: 0.7213\n",
      "has spend time 13m 10s/n\n",
      "val Loss: 0.5607 Acc: 0.6993\n",
      "has spend time 13m 11s/n\n",
      "\n",
      "Epoch 369/9999\n",
      "----------\n",
      "train Loss: 0.4935 Acc: 0.7664\n",
      "has spend time 13m 12s/n\n",
      "val Loss: 0.5503 Acc: 0.7059\n",
      "has spend time 13m 13s/n\n",
      "\n",
      "Epoch 370/9999\n",
      "----------\n",
      "train Loss: 0.5139 Acc: 0.7582\n",
      "has spend time 13m 14s/n\n",
      "val Loss: 0.5524 Acc: 0.7124\n",
      "has spend time 13m 15s/n\n",
      "\n",
      "Epoch 371/9999\n",
      "----------\n",
      "train Loss: 0.5166 Acc: 0.7131\n",
      "has spend time 13m 16s/n\n",
      "val Loss: 0.5393 Acc: 0.7190\n",
      "has spend time 13m 17s/n\n",
      "\n",
      "Epoch 372/9999\n",
      "----------\n",
      "train Loss: 0.5024 Acc: 0.7295\n",
      "has spend time 13m 18s/n\n",
      "val Loss: 0.5462 Acc: 0.7124\n",
      "has spend time 13m 19s/n\n",
      "\n",
      "Epoch 373/9999\n",
      "----------\n",
      "train Loss: 0.4735 Acc: 0.7664\n",
      "has spend time 13m 21s/n\n",
      "val Loss: 0.5449 Acc: 0.7190\n",
      "has spend time 13m 21s/n\n",
      "\n",
      "Epoch 374/9999\n",
      "----------\n",
      "train Loss: 0.5234 Acc: 0.7254\n",
      "has spend time 13m 23s/n\n",
      "val Loss: 0.5556 Acc: 0.6928\n",
      "has spend time 13m 23s/n\n",
      "\n",
      "Epoch 375/9999\n",
      "----------\n",
      "train Loss: 0.5003 Acc: 0.7705\n",
      "has spend time 13m 25s/n\n",
      "val Loss: 0.5591 Acc: 0.6993\n",
      "has spend time 13m 25s/n\n",
      "\n",
      "Epoch 376/9999\n",
      "----------\n",
      "train Loss: 0.4811 Acc: 0.7869\n",
      "has spend time 13m 27s/n\n",
      "val Loss: 0.5452 Acc: 0.7059\n",
      "has spend time 13m 27s/n\n",
      "\n",
      "Epoch 377/9999\n",
      "----------\n",
      "train Loss: 0.5299 Acc: 0.7213\n",
      "has spend time 13m 29s/n\n",
      "val Loss: 0.5427 Acc: 0.7124\n",
      "has spend time 13m 29s/n\n",
      "\n",
      "Epoch 378/9999\n",
      "----------\n",
      "train Loss: 0.5115 Acc: 0.7459\n",
      "has spend time 13m 31s/n\n",
      "val Loss: 0.5421 Acc: 0.7124\n",
      "has spend time 13m 31s/n\n",
      "\n",
      "Epoch 379/9999\n",
      "----------\n",
      "train Loss: 0.4786 Acc: 0.7664\n",
      "has spend time 13m 33s/n\n",
      "val Loss: 0.5479 Acc: 0.7190\n",
      "has spend time 13m 33s/n\n",
      "\n",
      "Epoch 380/9999\n",
      "----------\n",
      "train Loss: 0.4954 Acc: 0.7623\n",
      "has spend time 13m 35s/n\n",
      "val Loss: 0.5490 Acc: 0.7124\n",
      "has spend time 13m 36s/n\n",
      "\n",
      "Epoch 381/9999\n",
      "----------\n",
      "train Loss: 0.5120 Acc: 0.7336\n",
      "has spend time 13m 38s/n\n",
      "val Loss: 0.5505 Acc: 0.6993\n",
      "has spend time 13m 38s/n\n",
      "\n",
      "Epoch 382/9999\n",
      "----------\n",
      "train Loss: 0.4879 Acc: 0.7500\n",
      "has spend time 13m 40s/n\n",
      "val Loss: 0.5431 Acc: 0.7255\n",
      "has spend time 13m 40s/n\n",
      "\n",
      "Epoch 383/9999\n",
      "----------\n",
      "train Loss: 0.5010 Acc: 0.7459\n",
      "has spend time 13m 42s/n\n",
      "val Loss: 0.5581 Acc: 0.7059\n",
      "has spend time 13m 42s/n\n",
      "\n",
      "Epoch 384/9999\n",
      "----------\n",
      "train Loss: 0.5040 Acc: 0.7295\n",
      "has spend time 13m 44s/n\n",
      "val Loss: 0.5423 Acc: 0.7190\n",
      "has spend time 13m 45s/n\n",
      "\n",
      "Epoch 385/9999\n",
      "----------\n",
      "train Loss: 0.5365 Acc: 0.7131\n",
      "has spend time 13m 46s/n\n",
      "val Loss: 0.5384 Acc: 0.7255\n",
      "has spend time 13m 47s/n\n",
      "\n",
      "Epoch 386/9999\n",
      "----------\n",
      "train Loss: 0.4952 Acc: 0.7131\n",
      "has spend time 13m 48s/n\n",
      "val Loss: 0.5491 Acc: 0.7124\n",
      "has spend time 13m 49s/n\n",
      "\n",
      "Epoch 387/9999\n",
      "----------\n",
      "train Loss: 0.5170 Acc: 0.7500\n",
      "has spend time 13m 50s/n\n",
      "val Loss: 0.5436 Acc: 0.7190\n",
      "has spend time 13m 51s/n\n",
      "\n",
      "Epoch 388/9999\n",
      "----------\n",
      "train Loss: 0.5037 Acc: 0.7541\n",
      "has spend time 13m 52s/n\n",
      "val Loss: 0.5538 Acc: 0.7059\n",
      "has spend time 13m 53s/n\n",
      "\n",
      "Epoch 389/9999\n",
      "----------\n",
      "train Loss: 0.4856 Acc: 0.7459\n",
      "has spend time 13m 54s/n\n",
      "val Loss: 0.5656 Acc: 0.6928\n",
      "has spend time 13m 55s/n\n",
      "\n",
      "Epoch 390/9999\n",
      "----------\n",
      "train Loss: 0.5367 Acc: 0.7623\n",
      "has spend time 13m 56s/n\n",
      "val Loss: 0.5401 Acc: 0.7124\n",
      "has spend time 13m 57s/n\n",
      "\n",
      "Epoch 391/9999\n",
      "----------\n",
      "train Loss: 0.5222 Acc: 0.7008\n",
      "has spend time 13m 58s/n\n",
      "val Loss: 0.5454 Acc: 0.7059\n",
      "has spend time 13m 59s/n\n",
      "\n",
      "Epoch 392/9999\n",
      "----------\n",
      "train Loss: 0.5003 Acc: 0.7418\n",
      "has spend time 14m 1s/n\n",
      "val Loss: 0.5621 Acc: 0.7124\n",
      "has spend time 14m 1s/n\n",
      "\n",
      "Epoch 393/9999\n",
      "----------\n",
      "train Loss: 0.5583 Acc: 0.7049\n",
      "has spend time 14m 3s/n\n",
      "val Loss: 0.5470 Acc: 0.7124\n",
      "has spend time 14m 3s/n\n",
      "\n",
      "Epoch 394/9999\n",
      "----------\n",
      "train Loss: 0.5402 Acc: 0.7295\n",
      "has spend time 14m 5s/n\n",
      "val Loss: 0.5470 Acc: 0.7190\n",
      "has spend time 14m 5s/n\n",
      "\n",
      "Epoch 395/9999\n",
      "----------\n",
      "train Loss: 0.5036 Acc: 0.7377\n",
      "has spend time 14m 7s/n\n",
      "val Loss: 0.5486 Acc: 0.7124\n",
      "has spend time 14m 7s/n\n",
      "\n",
      "Epoch 396/9999\n",
      "----------\n",
      "train Loss: 0.5104 Acc: 0.7172\n",
      "has spend time 14m 9s/n\n",
      "val Loss: 0.5445 Acc: 0.7124\n",
      "has spend time 14m 10s/n\n",
      "\n",
      "Epoch 397/9999\n",
      "----------\n",
      "train Loss: 0.4779 Acc: 0.7787\n",
      "has spend time 14m 11s/n\n",
      "val Loss: 0.5476 Acc: 0.7124\n",
      "has spend time 14m 12s/n\n",
      "\n",
      "Epoch 398/9999\n",
      "----------\n",
      "train Loss: 0.4936 Acc: 0.7336\n",
      "has spend time 14m 14s/n\n",
      "val Loss: 0.5475 Acc: 0.7124\n",
      "has spend time 14m 14s/n\n",
      "\n",
      "Epoch 399/9999\n",
      "----------\n",
      "train Loss: 0.5000 Acc: 0.7582\n",
      "has spend time 14m 16s/n\n",
      "val Loss: 0.5542 Acc: 0.6928\n",
      "has spend time 14m 16s/n\n",
      "\n",
      "Epoch 400/9999\n",
      "----------\n",
      "train Loss: 0.5132 Acc: 0.7213\n",
      "has spend time 14m 18s/n\n",
      "val Loss: 0.5538 Acc: 0.7124\n",
      "has spend time 14m 19s/n\n",
      "\n",
      "Epoch 401/9999\n",
      "----------\n",
      "train Loss: 0.5047 Acc: 0.7623\n",
      "has spend time 14m 20s/n\n",
      "val Loss: 0.5579 Acc: 0.6928\n",
      "has spend time 14m 21s/n\n",
      "\n",
      "Epoch 402/9999\n",
      "----------\n",
      "train Loss: 0.5212 Acc: 0.7008\n",
      "has spend time 14m 22s/n\n",
      "val Loss: 0.5538 Acc: 0.6993\n",
      "has spend time 14m 23s/n\n",
      "\n",
      "Epoch 403/9999\n",
      "----------\n",
      "train Loss: 0.5010 Acc: 0.7295\n",
      "has spend time 14m 25s/n\n",
      "val Loss: 0.5719 Acc: 0.6993\n",
      "has spend time 14m 25s/n\n",
      "\n",
      "Epoch 404/9999\n",
      "----------\n",
      "train Loss: 0.5267 Acc: 0.7336\n",
      "has spend time 14m 27s/n\n",
      "val Loss: 0.5616 Acc: 0.6993\n",
      "has spend time 14m 27s/n\n",
      "\n",
      "Epoch 405/9999\n",
      "----------\n",
      "train Loss: 0.5018 Acc: 0.7500\n",
      "has spend time 14m 29s/n\n",
      "val Loss: 0.5538 Acc: 0.6928\n",
      "has spend time 14m 29s/n\n",
      "\n",
      "Epoch 406/9999\n",
      "----------\n",
      "train Loss: 0.5259 Acc: 0.7213\n",
      "has spend time 14m 31s/n\n",
      "val Loss: 0.5602 Acc: 0.6928\n",
      "has spend time 14m 31s/n\n",
      "\n",
      "Epoch 407/9999\n",
      "----------\n",
      "train Loss: 0.5203 Acc: 0.7172\n",
      "has spend time 14m 33s/n\n",
      "val Loss: 0.5463 Acc: 0.7190\n",
      "has spend time 14m 33s/n\n",
      "\n",
      "Epoch 408/9999\n",
      "----------\n",
      "train Loss: 0.5122 Acc: 0.7172\n",
      "has spend time 14m 35s/n\n",
      "val Loss: 0.5476 Acc: 0.7124\n",
      "has spend time 14m 35s/n\n",
      "\n",
      "Epoch 409/9999\n",
      "----------\n",
      "train Loss: 0.5073 Acc: 0.7459\n",
      "has spend time 14m 37s/n\n",
      "val Loss: 0.5549 Acc: 0.7190\n",
      "has spend time 14m 38s/n\n",
      "\n",
      "Epoch 410/9999\n",
      "----------\n",
      "train Loss: 0.4994 Acc: 0.7172\n",
      "has spend time 14m 39s/n\n",
      "val Loss: 0.5526 Acc: 0.7124\n",
      "has spend time 14m 40s/n\n",
      "\n",
      "Epoch 411/9999\n",
      "----------\n",
      "train Loss: 0.5124 Acc: 0.7254\n",
      "has spend time 14m 41s/n\n",
      "val Loss: 0.5562 Acc: 0.7059\n",
      "has spend time 14m 42s/n\n",
      "\n",
      "Epoch 412/9999\n",
      "----------\n",
      "train Loss: 0.4836 Acc: 0.7664\n",
      "has spend time 14m 43s/n\n",
      "val Loss: 0.5468 Acc: 0.7124\n",
      "has spend time 14m 44s/n\n",
      "\n",
      "Epoch 413/9999\n",
      "----------\n",
      "train Loss: 0.4845 Acc: 0.7705\n",
      "has spend time 14m 45s/n\n",
      "val Loss: 0.5604 Acc: 0.7190\n",
      "has spend time 14m 46s/n\n",
      "\n",
      "Epoch 414/9999\n",
      "----------\n",
      "train Loss: 0.5279 Acc: 0.7090\n",
      "has spend time 14m 47s/n\n",
      "val Loss: 0.5513 Acc: 0.7124\n",
      "has spend time 14m 48s/n\n",
      "\n",
      "Epoch 415/9999\n",
      "----------\n",
      "train Loss: 0.5132 Acc: 0.7254\n",
      "has spend time 14m 49s/n\n",
      "val Loss: 0.5490 Acc: 0.7124\n",
      "has spend time 14m 50s/n\n",
      "\n",
      "Epoch 416/9999\n",
      "----------\n",
      "train Loss: 0.4948 Acc: 0.7418\n",
      "has spend time 14m 51s/n\n",
      "val Loss: 0.5570 Acc: 0.7124\n",
      "has spend time 14m 52s/n\n",
      "\n",
      "Epoch 417/9999\n",
      "----------\n",
      "train Loss: 0.4983 Acc: 0.7418\n",
      "has spend time 14m 53s/n\n",
      "val Loss: 0.5497 Acc: 0.6993\n",
      "has spend time 14m 54s/n\n",
      "\n",
      "Epoch 418/9999\n",
      "----------\n",
      "train Loss: 0.4688 Acc: 0.7541\n",
      "has spend time 14m 56s/n\n",
      "val Loss: 0.5422 Acc: 0.7059\n",
      "has spend time 14m 57s/n\n",
      "\n",
      "Epoch 419/9999\n",
      "----------\n",
      "train Loss: 0.5195 Acc: 0.7213\n",
      "has spend time 14m 58s/n\n",
      "val Loss: 0.5494 Acc: 0.7124\n",
      "has spend time 14m 59s/n\n",
      "\n",
      "Epoch 420/9999\n",
      "----------\n",
      "train Loss: 0.5077 Acc: 0.7582\n",
      "has spend time 15m 0s/n\n",
      "val Loss: 0.5514 Acc: 0.7059\n",
      "has spend time 15m 1s/n\n",
      "\n",
      "Epoch 421/9999\n",
      "----------\n",
      "train Loss: 0.5008 Acc: 0.7582\n",
      "has spend time 15m 2s/n\n",
      "val Loss: 0.5589 Acc: 0.7124\n",
      "has spend time 15m 3s/n\n",
      "\n",
      "Epoch 422/9999\n",
      "----------\n",
      "train Loss: 0.5368 Acc: 0.6926\n",
      "has spend time 15m 4s/n\n",
      "val Loss: 0.5606 Acc: 0.6993\n",
      "has spend time 15m 5s/n\n",
      "\n",
      "Epoch 423/9999\n",
      "----------\n",
      "train Loss: 0.5203 Acc: 0.7131\n",
      "has spend time 15m 6s/n\n",
      "val Loss: 0.5536 Acc: 0.6993\n",
      "has spend time 15m 7s/n\n",
      "\n",
      "Epoch 424/9999\n",
      "----------\n",
      "train Loss: 0.4987 Acc: 0.7582\n",
      "has spend time 15m 9s/n\n",
      "val Loss: 0.5537 Acc: 0.6993\n",
      "has spend time 15m 10s/n\n",
      "\n",
      "Epoch 425/9999\n",
      "----------\n",
      "train Loss: 0.5331 Acc: 0.7172\n",
      "has spend time 15m 11s/n\n",
      "val Loss: 0.5499 Acc: 0.7124\n",
      "has spend time 15m 12s/n\n",
      "\n",
      "Epoch 426/9999\n",
      "----------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train Loss: 0.5019 Acc: 0.7500\n",
      "has spend time 15m 13s/n\n",
      "val Loss: 0.5465 Acc: 0.7124\n",
      "has spend time 15m 14s/n\n",
      "\n",
      "Epoch 427/9999\n",
      "----------\n",
      "train Loss: 0.5054 Acc: 0.7377\n",
      "has spend time 15m 15s/n\n",
      "val Loss: 0.5445 Acc: 0.7124\n",
      "has spend time 15m 16s/n\n",
      "\n",
      "Epoch 428/9999\n",
      "----------\n",
      "train Loss: 0.5245 Acc: 0.7336\n",
      "has spend time 15m 17s/n\n",
      "val Loss: 0.5439 Acc: 0.7190\n",
      "has spend time 15m 18s/n\n",
      "\n",
      "Epoch 429/9999\n",
      "----------\n",
      "train Loss: 0.4915 Acc: 0.7500\n",
      "has spend time 15m 20s/n\n",
      "val Loss: 0.5496 Acc: 0.7124\n",
      "has spend time 15m 20s/n\n",
      "\n",
      "Epoch 430/9999\n",
      "----------\n",
      "train Loss: 0.5195 Acc: 0.7172\n",
      "has spend time 15m 22s/n\n",
      "val Loss: 0.5503 Acc: 0.7190\n",
      "has spend time 15m 22s/n\n",
      "\n",
      "Epoch 431/9999\n",
      "----------\n",
      "train Loss: 0.5187 Acc: 0.7459\n",
      "has spend time 15m 24s/n\n",
      "val Loss: 0.5438 Acc: 0.7124\n",
      "has spend time 15m 25s/n\n",
      "\n",
      "Epoch 432/9999\n",
      "----------\n",
      "train Loss: 0.4816 Acc: 0.7705\n",
      "has spend time 15m 26s/n\n",
      "val Loss: 0.5466 Acc: 0.7124\n",
      "has spend time 15m 27s/n\n",
      "\n",
      "Epoch 433/9999\n",
      "----------\n",
      "train Loss: 0.4777 Acc: 0.7500\n",
      "has spend time 15m 29s/n\n",
      "val Loss: 0.5702 Acc: 0.6993\n",
      "has spend time 15m 30s/n\n",
      "\n",
      "Epoch 434/9999\n",
      "----------\n",
      "train Loss: 0.4806 Acc: 0.7705\n",
      "has spend time 15m 31s/n\n",
      "val Loss: 0.5590 Acc: 0.6863\n",
      "has spend time 15m 32s/n\n",
      "\n",
      "Epoch 435/9999\n",
      "----------\n",
      "train Loss: 0.4903 Acc: 0.7828\n",
      "has spend time 15m 33s/n\n",
      "val Loss: 0.5578 Acc: 0.6928\n",
      "has spend time 15m 34s/n\n",
      "\n",
      "Epoch 436/9999\n",
      "----------\n",
      "train Loss: 0.5122 Acc: 0.7254\n",
      "has spend time 15m 35s/n\n",
      "val Loss: 0.5550 Acc: 0.6993\n",
      "has spend time 15m 36s/n\n",
      "\n",
      "Epoch 437/9999\n",
      "----------\n",
      "train Loss: 0.4890 Acc: 0.7418\n",
      "has spend time 15m 37s/n\n",
      "val Loss: 0.5418 Acc: 0.7190\n",
      "has spend time 15m 38s/n\n",
      "\n",
      "Epoch 438/9999\n",
      "----------\n",
      "train Loss: 0.4865 Acc: 0.7582\n",
      "has spend time 15m 39s/n\n",
      "val Loss: 0.5600 Acc: 0.6993\n",
      "has spend time 15m 40s/n\n",
      "\n",
      "Epoch 439/9999\n",
      "----------\n",
      "train Loss: 0.4855 Acc: 0.7582\n",
      "has spend time 15m 42s/n\n",
      "val Loss: 0.5441 Acc: 0.7124\n",
      "has spend time 15m 42s/n\n",
      "\n",
      "Epoch 440/9999\n",
      "----------\n",
      "train Loss: 0.5054 Acc: 0.7500\n",
      "has spend time 15m 44s/n\n",
      "val Loss: 0.5580 Acc: 0.6928\n",
      "has spend time 15m 44s/n\n",
      "\n",
      "Epoch 441/9999\n",
      "----------\n",
      "train Loss: 0.5164 Acc: 0.7336\n",
      "has spend time 15m 46s/n\n",
      "val Loss: 0.5487 Acc: 0.7255\n",
      "has spend time 15m 46s/n\n",
      "\n",
      "Epoch 442/9999\n",
      "----------\n",
      "train Loss: 0.5216 Acc: 0.7336\n",
      "has spend time 15m 48s/n\n",
      "val Loss: 0.5495 Acc: 0.7124\n",
      "has spend time 15m 48s/n\n",
      "\n",
      "Epoch 443/9999\n",
      "----------\n",
      "train Loss: 0.5048 Acc: 0.7377\n",
      "has spend time 15m 50s/n\n",
      "val Loss: 0.5544 Acc: 0.7059\n",
      "has spend time 15m 51s/n\n",
      "\n",
      "Epoch 444/9999\n",
      "----------\n",
      "train Loss: 0.5234 Acc: 0.7541\n",
      "has spend time 15m 52s/n\n",
      "val Loss: 0.5634 Acc: 0.6863\n",
      "has spend time 15m 53s/n\n",
      "\n",
      "Epoch 445/9999\n",
      "----------\n",
      "train Loss: 0.5385 Acc: 0.7008\n",
      "has spend time 15m 54s/n\n",
      "val Loss: 0.5519 Acc: 0.6928\n",
      "has spend time 15m 55s/n\n",
      "\n",
      "Epoch 446/9999\n",
      "----------\n",
      "train Loss: 0.5117 Acc: 0.7254\n",
      "has spend time 15m 57s/n\n",
      "val Loss: 0.5568 Acc: 0.6863\n",
      "has spend time 15m 57s/n\n",
      "\n",
      "Epoch 447/9999\n",
      "----------\n",
      "train Loss: 0.5023 Acc: 0.7295\n",
      "has spend time 15m 59s/n\n",
      "val Loss: 0.5536 Acc: 0.6928\n",
      "has spend time 15m 59s/n\n",
      "\n",
      "Epoch 448/9999\n",
      "----------\n",
      "train Loss: 0.4967 Acc: 0.7254\n",
      "has spend time 16m 1s/n\n",
      "val Loss: 0.5544 Acc: 0.7059\n",
      "has spend time 16m 1s/n\n",
      "\n",
      "Epoch 449/9999\n",
      "----------\n",
      "train Loss: 0.5042 Acc: 0.7254\n",
      "has spend time 16m 3s/n\n",
      "val Loss: 0.5448 Acc: 0.7255\n",
      "has spend time 16m 3s/n\n",
      "\n",
      "Epoch 450/9999\n",
      "----------\n",
      "train Loss: 0.5257 Acc: 0.7295\n",
      "has spend time 16m 5s/n\n",
      "val Loss: 0.5422 Acc: 0.7190\n",
      "has spend time 16m 6s/n\n",
      "\n",
      "Epoch 451/9999\n",
      "----------\n",
      "train Loss: 0.5075 Acc: 0.7377\n",
      "has spend time 16m 7s/n\n",
      "val Loss: 0.5451 Acc: 0.6928\n",
      "has spend time 16m 8s/n\n",
      "\n",
      "Epoch 452/9999\n",
      "----------\n",
      "train Loss: 0.5077 Acc: 0.7500\n",
      "has spend time 16m 9s/n\n",
      "val Loss: 0.5437 Acc: 0.7124\n",
      "has spend time 16m 10s/n\n",
      "\n",
      "Epoch 453/9999\n",
      "----------\n",
      "train Loss: 0.4557 Acc: 0.7705\n",
      "has spend time 16m 11s/n\n",
      "val Loss: 0.5457 Acc: 0.7190\n",
      "has spend time 16m 12s/n\n",
      "\n",
      "Epoch 454/9999\n",
      "----------\n",
      "train Loss: 0.4911 Acc: 0.7500\n",
      "has spend time 16m 13s/n\n",
      "val Loss: 0.5340 Acc: 0.7255\n",
      "has spend time 16m 14s/n\n",
      "\n",
      "Epoch 455/9999\n",
      "----------\n",
      "train Loss: 0.5157 Acc: 0.7254\n",
      "has spend time 16m 16s/n\n",
      "val Loss: 0.5397 Acc: 0.7190\n",
      "has spend time 16m 17s/n\n",
      "\n",
      "Epoch 456/9999\n",
      "----------\n",
      "train Loss: 0.5161 Acc: 0.7377\n",
      "has spend time 16m 18s/n\n",
      "val Loss: 0.5418 Acc: 0.7255\n",
      "has spend time 16m 19s/n\n",
      "\n",
      "Epoch 457/9999\n",
      "----------\n",
      "train Loss: 0.4814 Acc: 0.7705\n",
      "has spend time 16m 21s/n\n",
      "val Loss: 0.5508 Acc: 0.7124\n",
      "has spend time 16m 21s/n\n",
      "\n",
      "Epoch 458/9999\n",
      "----------\n",
      "train Loss: 0.5221 Acc: 0.7377\n",
      "has spend time 16m 23s/n\n",
      "val Loss: 0.5526 Acc: 0.7059\n",
      "has spend time 16m 23s/n\n",
      "\n",
      "Epoch 459/9999\n",
      "----------\n",
      "train Loss: 0.5067 Acc: 0.7336\n",
      "has spend time 16m 25s/n\n",
      "val Loss: 0.5549 Acc: 0.6928\n",
      "has spend time 16m 26s/n\n",
      "\n",
      "Epoch 460/9999\n",
      "----------\n",
      "train Loss: 0.5318 Acc: 0.7295\n",
      "has spend time 16m 27s/n\n",
      "val Loss: 0.5538 Acc: 0.6863\n",
      "has spend time 16m 28s/n\n",
      "\n",
      "Epoch 461/9999\n",
      "----------\n",
      "train Loss: 0.5010 Acc: 0.7254\n",
      "has spend time 16m 29s/n\n",
      "val Loss: 0.5537 Acc: 0.6928\n",
      "has spend time 16m 30s/n\n",
      "\n",
      "Epoch 462/9999\n",
      "----------\n",
      "train Loss: 0.5012 Acc: 0.7418\n",
      "has spend time 16m 31s/n\n",
      "val Loss: 0.5467 Acc: 0.6863\n",
      "has spend time 16m 32s/n\n",
      "\n",
      "Epoch 463/9999\n",
      "----------\n",
      "train Loss: 0.4883 Acc: 0.7418\n",
      "has spend time 16m 33s/n\n",
      "val Loss: 0.5503 Acc: 0.6993\n",
      "has spend time 16m 34s/n\n",
      "\n",
      "Epoch 464/9999\n",
      "----------\n",
      "train Loss: 0.4909 Acc: 0.7500\n",
      "has spend time 16m 35s/n\n",
      "val Loss: 0.5616 Acc: 0.6863\n",
      "has spend time 16m 36s/n\n",
      "\n",
      "Epoch 465/9999\n",
      "----------\n",
      "train Loss: 0.5270 Acc: 0.7090\n",
      "has spend time 16m 37s/n\n",
      "val Loss: 0.5468 Acc: 0.7124\n",
      "has spend time 16m 38s/n\n",
      "\n",
      "Epoch 466/9999\n",
      "----------\n",
      "train Loss: 0.5432 Acc: 0.6967\n",
      "has spend time 16m 40s/n\n",
      "val Loss: 0.5439 Acc: 0.7190\n",
      "has spend time 16m 40s/n\n",
      "\n",
      "Epoch 467/9999\n",
      "----------\n",
      "train Loss: 0.4886 Acc: 0.7705\n",
      "has spend time 16m 42s/n\n",
      "val Loss: 0.5401 Acc: 0.7124\n",
      "has spend time 16m 42s/n\n",
      "\n",
      "Epoch 468/9999\n",
      "----------\n",
      "train Loss: 0.5097 Acc: 0.7377\n",
      "has spend time 16m 44s/n\n",
      "val Loss: 0.5455 Acc: 0.7124\n",
      "has spend time 16m 44s/n\n",
      "\n",
      "Epoch 469/9999\n",
      "----------\n",
      "train Loss: 0.5244 Acc: 0.7254\n",
      "has spend time 16m 46s/n\n",
      "val Loss: 0.5593 Acc: 0.6993\n",
      "has spend time 16m 46s/n\n",
      "\n",
      "Epoch 470/9999\n",
      "----------\n",
      "train Loss: 0.5110 Acc: 0.7377\n",
      "has spend time 16m 48s/n\n",
      "val Loss: 0.5487 Acc: 0.7124\n",
      "has spend time 16m 48s/n\n",
      "\n",
      "Epoch 471/9999\n",
      "----------\n",
      "train Loss: 0.5108 Acc: 0.7254\n",
      "has spend time 16m 50s/n\n",
      "val Loss: 0.5504 Acc: 0.7124\n",
      "has spend time 16m 51s/n\n",
      "\n",
      "Epoch 472/9999\n",
      "----------\n",
      "train Loss: 0.5401 Acc: 0.7008\n",
      "has spend time 16m 52s/n\n",
      "val Loss: 0.5457 Acc: 0.7255\n",
      "has spend time 16m 53s/n\n",
      "\n",
      "Epoch 473/9999\n",
      "----------\n",
      "train Loss: 0.5065 Acc: 0.7172\n",
      "has spend time 16m 54s/n\n",
      "val Loss: 0.5541 Acc: 0.6993\n",
      "has spend time 16m 55s/n\n",
      "\n",
      "Epoch 474/9999\n",
      "----------\n",
      "train Loss: 0.5058 Acc: 0.7500\n",
      "has spend time 16m 57s/n\n",
      "val Loss: 0.5741 Acc: 0.6863\n",
      "has spend time 16m 57s/n\n",
      "\n",
      "Epoch 475/9999\n",
      "----------\n",
      "train Loss: 0.5067 Acc: 0.7500\n",
      "has spend time 16m 59s/n\n",
      "val Loss: 0.5504 Acc: 0.7190\n",
      "has spend time 16m 59s/n\n",
      "\n",
      "Epoch 476/9999\n",
      "----------\n",
      "train Loss: 0.4751 Acc: 0.7787\n",
      "has spend time 17m 1s/n\n",
      "val Loss: 0.5518 Acc: 0.6993\n",
      "has spend time 17m 2s/n\n",
      "\n",
      "Epoch 477/9999\n",
      "----------\n",
      "train Loss: 0.5088 Acc: 0.7459\n",
      "has spend time 17m 3s/n\n",
      "val Loss: 0.5519 Acc: 0.6993\n",
      "has spend time 17m 4s/n\n",
      "\n",
      "Epoch 478/9999\n",
      "----------\n",
      "train Loss: 0.5211 Acc: 0.7254\n",
      "has spend time 17m 5s/n\n",
      "val Loss: 0.5612 Acc: 0.6993\n",
      "has spend time 17m 6s/n\n",
      "\n",
      "Epoch 479/9999\n",
      "----------\n",
      "train Loss: 0.4991 Acc: 0.7377\n",
      "has spend time 17m 7s/n\n",
      "val Loss: 0.5623 Acc: 0.6863\n",
      "has spend time 17m 8s/n\n",
      "\n",
      "Epoch 480/9999\n",
      "----------\n",
      "train Loss: 0.5217 Acc: 0.7377\n",
      "has spend time 17m 10s/n\n",
      "val Loss: 0.5538 Acc: 0.7059\n",
      "has spend time 17m 10s/n\n",
      "\n",
      "Epoch 481/9999\n",
      "----------\n",
      "train Loss: 0.5439 Acc: 0.7090\n",
      "has spend time 17m 12s/n\n",
      "val Loss: 0.5462 Acc: 0.7124\n",
      "has spend time 17m 12s/n\n",
      "\n",
      "Epoch 482/9999\n",
      "----------\n",
      "train Loss: 0.5199 Acc: 0.7418\n",
      "has spend time 17m 14s/n\n",
      "val Loss: 0.5472 Acc: 0.7059\n",
      "has spend time 17m 14s/n\n",
      "\n",
      "Epoch 483/9999\n",
      "----------\n",
      "train Loss: 0.4985 Acc: 0.7418\n",
      "has spend time 17m 16s/n\n",
      "val Loss: 0.5545 Acc: 0.6993\n",
      "has spend time 17m 16s/n\n",
      "\n",
      "Epoch 484/9999\n",
      "----------\n",
      "train Loss: 0.5208 Acc: 0.7459\n",
      "has spend time 17m 18s/n\n",
      "val Loss: 0.5659 Acc: 0.6993\n",
      "has spend time 17m 18s/n\n",
      "\n",
      "Epoch 485/9999\n",
      "----------\n",
      "train Loss: 0.5254 Acc: 0.7090\n",
      "has spend time 17m 20s/n\n",
      "val Loss: 0.5520 Acc: 0.7059\n",
      "has spend time 17m 20s/n\n",
      "\n",
      "Epoch 486/9999\n",
      "----------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train Loss: 0.4655 Acc: 0.7787\n",
      "has spend time 17m 22s/n\n",
      "val Loss: 0.5554 Acc: 0.6993\n",
      "has spend time 17m 22s/n\n",
      "\n",
      "Epoch 487/9999\n",
      "----------\n",
      "train Loss: 0.5080 Acc: 0.7377\n",
      "has spend time 17m 24s/n\n",
      "val Loss: 0.5676 Acc: 0.6928\n",
      "has spend time 17m 24s/n\n",
      "\n",
      "Epoch 488/9999\n",
      "----------\n",
      "train Loss: 0.5007 Acc: 0.7500\n",
      "has spend time 17m 26s/n\n",
      "val Loss: 0.5581 Acc: 0.6993\n",
      "has spend time 17m 27s/n\n",
      "\n",
      "Epoch 489/9999\n",
      "----------\n",
      "train Loss: 0.5316 Acc: 0.7131\n",
      "has spend time 17m 28s/n\n",
      "val Loss: 0.5519 Acc: 0.7190\n",
      "has spend time 17m 29s/n\n",
      "\n",
      "Epoch 490/9999\n",
      "----------\n",
      "train Loss: 0.4762 Acc: 0.7377\n",
      "has spend time 17m 30s/n\n",
      "val Loss: 0.5525 Acc: 0.7124\n",
      "has spend time 17m 31s/n\n",
      "\n",
      "Epoch 491/9999\n",
      "----------\n",
      "train Loss: 0.5295 Acc: 0.7008\n",
      "has spend time 17m 32s/n\n",
      "val Loss: 0.5514 Acc: 0.7124\n",
      "has spend time 17m 33s/n\n",
      "\n",
      "Epoch 492/9999\n",
      "----------\n",
      "train Loss: 0.5117 Acc: 0.7295\n",
      "has spend time 17m 34s/n\n",
      "val Loss: 0.5471 Acc: 0.7124\n",
      "has spend time 17m 35s/n\n",
      "\n",
      "Epoch 493/9999\n",
      "----------\n",
      "train Loss: 0.5569 Acc: 0.7459\n",
      "has spend time 17m 36s/n\n",
      "val Loss: 0.5484 Acc: 0.7124\n",
      "has spend time 17m 37s/n\n",
      "\n",
      "Epoch 494/9999\n",
      "----------\n",
      "train Loss: 0.5054 Acc: 0.7459\n",
      "has spend time 17m 38s/n\n",
      "val Loss: 0.5403 Acc: 0.7124\n",
      "has spend time 17m 39s/n\n",
      "\n",
      "Epoch 495/9999\n",
      "----------\n",
      "train Loss: 0.5134 Acc: 0.7418\n",
      "has spend time 17m 41s/n\n",
      "val Loss: 0.5467 Acc: 0.7059\n",
      "has spend time 17m 41s/n\n",
      "\n",
      "Epoch 496/9999\n",
      "----------\n",
      "train Loss: 0.5275 Acc: 0.7213\n",
      "has spend time 17m 43s/n\n",
      "val Loss: 0.5483 Acc: 0.7190\n",
      "has spend time 17m 44s/n\n",
      "\n",
      "Epoch 497/9999\n",
      "----------\n",
      "train Loss: 0.4981 Acc: 0.7295\n",
      "has spend time 17m 45s/n\n",
      "val Loss: 0.5564 Acc: 0.6993\n",
      "has spend time 17m 46s/n\n",
      "\n",
      "Epoch 498/9999\n",
      "----------\n",
      "train Loss: 0.5012 Acc: 0.7213\n",
      "has spend time 17m 47s/n\n",
      "val Loss: 0.5476 Acc: 0.7124\n",
      "has spend time 17m 48s/n\n",
      "\n",
      "Epoch 499/9999\n",
      "----------\n",
      "train Loss: 0.5014 Acc: 0.7254\n",
      "has spend time 17m 49s/n\n",
      "val Loss: 0.5487 Acc: 0.6993\n",
      "has spend time 17m 50s/n\n",
      "\n",
      "Epoch 500/9999\n",
      "----------\n",
      "train Loss: 0.5576 Acc: 0.7213\n",
      "has spend time 17m 51s/n\n",
      "val Loss: 0.5425 Acc: 0.7190\n",
      "has spend time 17m 52s/n\n",
      "\n",
      "Epoch 501/9999\n",
      "----------\n",
      "train Loss: 0.5179 Acc: 0.7213\n",
      "has spend time 17m 54s/n\n",
      "val Loss: 0.5488 Acc: 0.7059\n",
      "has spend time 17m 54s/n\n",
      "\n",
      "Epoch 502/9999\n",
      "----------\n",
      "train Loss: 0.5032 Acc: 0.7377\n",
      "has spend time 17m 56s/n\n",
      "val Loss: 0.5490 Acc: 0.7190\n",
      "has spend time 17m 56s/n\n",
      "\n",
      "Epoch 503/9999\n",
      "----------\n",
      "train Loss: 0.4819 Acc: 0.7541\n",
      "has spend time 17m 58s/n\n",
      "val Loss: 0.5570 Acc: 0.7059\n",
      "has spend time 17m 59s/n\n",
      "\n",
      "Epoch 504/9999\n",
      "----------\n",
      "train Loss: 0.4986 Acc: 0.7295\n",
      "has spend time 18m 0s/n\n",
      "val Loss: 0.5573 Acc: 0.7059\n",
      "has spend time 18m 1s/n\n",
      "\n",
      "Epoch 505/9999\n",
      "----------\n",
      "train Loss: 0.5122 Acc: 0.7090\n",
      "has spend time 18m 2s/n\n",
      "val Loss: 0.5554 Acc: 0.7124\n",
      "has spend time 18m 3s/n\n",
      "\n",
      "Epoch 506/9999\n",
      "----------\n",
      "train Loss: 0.5210 Acc: 0.7664\n",
      "has spend time 18m 4s/n\n",
      "val Loss: 0.5534 Acc: 0.6993\n",
      "has spend time 18m 5s/n\n",
      "\n",
      "Epoch 507/9999\n",
      "----------\n",
      "train Loss: 0.4966 Acc: 0.7377\n",
      "has spend time 18m 6s/n\n",
      "val Loss: 0.5468 Acc: 0.7124\n",
      "has spend time 18m 7s/n\n",
      "\n",
      "Epoch 508/9999\n",
      "----------\n",
      "train Loss: 0.5297 Acc: 0.6926\n",
      "has spend time 18m 9s/n\n",
      "val Loss: 0.5525 Acc: 0.7059\n",
      "has spend time 18m 9s/n\n",
      "\n",
      "Epoch 509/9999\n",
      "----------\n",
      "train Loss: 0.5005 Acc: 0.7541\n",
      "has spend time 18m 11s/n\n",
      "val Loss: 0.5519 Acc: 0.7059\n",
      "has spend time 18m 11s/n\n",
      "\n",
      "Epoch 510/9999\n",
      "----------\n",
      "train Loss: 0.4901 Acc: 0.7746\n",
      "has spend time 18m 13s/n\n",
      "val Loss: 0.5577 Acc: 0.7059\n",
      "has spend time 18m 13s/n\n",
      "\n",
      "Epoch 511/9999\n",
      "----------\n",
      "train Loss: 0.5002 Acc: 0.7418\n",
      "has spend time 18m 15s/n\n",
      "val Loss: 0.5526 Acc: 0.7124\n",
      "has spend time 18m 15s/n\n",
      "\n",
      "Epoch 512/9999\n",
      "----------\n",
      "train Loss: 0.5094 Acc: 0.7172\n",
      "has spend time 18m 17s/n\n",
      "val Loss: 0.5582 Acc: 0.7059\n",
      "has spend time 18m 17s/n\n",
      "\n",
      "Epoch 513/9999\n",
      "----------\n",
      "train Loss: 0.4960 Acc: 0.7541\n",
      "has spend time 18m 19s/n\n",
      "val Loss: 0.5469 Acc: 0.7059\n",
      "has spend time 18m 19s/n\n",
      "\n",
      "Epoch 514/9999\n",
      "----------\n",
      "train Loss: 0.5183 Acc: 0.7172\n",
      "has spend time 18m 21s/n\n",
      "val Loss: 0.5452 Acc: 0.7059\n",
      "has spend time 18m 21s/n\n",
      "\n",
      "Epoch 515/9999\n",
      "----------\n",
      "train Loss: 0.5391 Acc: 0.7090\n",
      "has spend time 18m 23s/n\n",
      "val Loss: 0.5432 Acc: 0.7255\n",
      "has spend time 18m 23s/n\n",
      "\n",
      "Epoch 516/9999\n",
      "----------\n",
      "train Loss: 0.5569 Acc: 0.6926\n",
      "has spend time 18m 25s/n\n",
      "val Loss: 0.5490 Acc: 0.7190\n",
      "has spend time 18m 26s/n\n",
      "\n",
      "Epoch 517/9999\n",
      "----------\n",
      "train Loss: 0.5223 Acc: 0.7008\n",
      "has spend time 18m 27s/n\n",
      "val Loss: 0.5533 Acc: 0.7059\n",
      "has spend time 18m 28s/n\n",
      "\n",
      "Epoch 518/9999\n",
      "----------\n",
      "train Loss: 0.4959 Acc: 0.7541\n",
      "has spend time 18m 29s/n\n",
      "val Loss: 0.5494 Acc: 0.7059\n",
      "has spend time 18m 30s/n\n",
      "\n",
      "Epoch 519/9999\n",
      "----------\n",
      "train Loss: 0.5165 Acc: 0.7172\n",
      "has spend time 18m 32s/n\n",
      "val Loss: 0.5523 Acc: 0.7059\n",
      "has spend time 18m 32s/n\n",
      "\n",
      "Epoch 520/9999\n",
      "----------\n",
      "train Loss: 0.4851 Acc: 0.7623\n",
      "has spend time 18m 34s/n\n",
      "val Loss: 0.5577 Acc: 0.7059\n",
      "has spend time 18m 35s/n\n",
      "\n",
      "Epoch 521/9999\n",
      "----------\n",
      "train Loss: 0.4885 Acc: 0.7664\n",
      "has spend time 18m 36s/n\n",
      "val Loss: 0.5450 Acc: 0.7059\n",
      "has spend time 18m 37s/n\n",
      "\n",
      "Epoch 522/9999\n",
      "----------\n",
      "train Loss: 0.5031 Acc: 0.7377\n",
      "has spend time 18m 38s/n\n",
      "val Loss: 0.5489 Acc: 0.7124\n",
      "has spend time 18m 39s/n\n",
      "\n",
      "Epoch 523/9999\n",
      "----------\n",
      "train Loss: 0.5387 Acc: 0.7090\n",
      "has spend time 18m 40s/n\n",
      "val Loss: 0.5454 Acc: 0.7059\n",
      "has spend time 18m 41s/n\n",
      "\n",
      "Epoch 524/9999\n",
      "----------\n",
      "train Loss: 0.5052 Acc: 0.7541\n",
      "has spend time 18m 42s/n\n",
      "val Loss: 0.5557 Acc: 0.6928\n",
      "has spend time 18m 43s/n\n",
      "\n",
      "Epoch 525/9999\n",
      "----------\n",
      "train Loss: 0.5142 Acc: 0.7500\n",
      "has spend time 18m 44s/n\n",
      "val Loss: 0.5438 Acc: 0.7124\n",
      "has spend time 18m 45s/n\n",
      "\n",
      "Epoch 526/9999\n",
      "----------\n",
      "train Loss: 0.5004 Acc: 0.7172\n",
      "has spend time 18m 47s/n\n",
      "val Loss: 0.5405 Acc: 0.7255\n",
      "has spend time 18m 47s/n\n",
      "\n",
      "Epoch 527/9999\n",
      "----------\n",
      "train Loss: 0.5554 Acc: 0.6803\n",
      "has spend time 18m 49s/n\n",
      "val Loss: 0.5692 Acc: 0.6928\n",
      "has spend time 18m 49s/n\n",
      "\n",
      "Epoch 528/9999\n",
      "----------\n",
      "train Loss: 0.5160 Acc: 0.7172\n",
      "has spend time 18m 51s/n\n",
      "val Loss: 0.5545 Acc: 0.7059\n",
      "has spend time 18m 51s/n\n",
      "\n",
      "Epoch 529/9999\n",
      "----------\n",
      "train Loss: 0.4999 Acc: 0.7377\n",
      "has spend time 18m 53s/n\n",
      "val Loss: 0.5590 Acc: 0.7124\n",
      "has spend time 18m 53s/n\n",
      "\n",
      "Epoch 530/9999\n",
      "----------\n",
      "train Loss: 0.4852 Acc: 0.7459\n",
      "has spend time 18m 55s/n\n",
      "val Loss: 0.5463 Acc: 0.7190\n",
      "has spend time 18m 56s/n\n",
      "\n",
      "Epoch 531/9999\n",
      "----------\n",
      "train Loss: 0.5044 Acc: 0.7664\n",
      "has spend time 18m 57s/n\n",
      "val Loss: 0.5404 Acc: 0.7190\n",
      "has spend time 18m 58s/n\n",
      "\n",
      "Epoch 532/9999\n",
      "----------\n",
      "train Loss: 0.5029 Acc: 0.7336\n",
      "has spend time 18m 59s/n\n",
      "val Loss: 0.5437 Acc: 0.7124\n",
      "has spend time 18m 60s/n\n",
      "\n",
      "Epoch 533/9999\n",
      "----------\n",
      "train Loss: 0.5076 Acc: 0.7623\n",
      "has spend time 19m 1s/n\n",
      "val Loss: 0.5441 Acc: 0.7320\n",
      "has spend time 19m 2s/n\n",
      "\n",
      "Epoch 534/9999\n",
      "----------\n",
      "train Loss: 0.5216 Acc: 0.6926\n",
      "has spend time 19m 3s/n\n",
      "val Loss: 0.5427 Acc: 0.7124\n",
      "has spend time 19m 4s/n\n",
      "\n",
      "Epoch 535/9999\n",
      "----------\n",
      "train Loss: 0.5042 Acc: 0.7377\n",
      "has spend time 19m 6s/n\n",
      "val Loss: 0.5530 Acc: 0.7059\n",
      "has spend time 19m 6s/n\n",
      "\n",
      "Epoch 536/9999\n",
      "----------\n",
      "train Loss: 0.5007 Acc: 0.7541\n",
      "has spend time 19m 8s/n\n",
      "val Loss: 0.5589 Acc: 0.6993\n",
      "has spend time 19m 8s/n\n",
      "\n",
      "Epoch 537/9999\n",
      "----------\n",
      "train Loss: 0.4943 Acc: 0.7541\n",
      "has spend time 19m 10s/n\n",
      "val Loss: 0.5474 Acc: 0.7059\n",
      "has spend time 19m 11s/n\n",
      "\n",
      "Epoch 538/9999\n",
      "----------\n",
      "train Loss: 0.4997 Acc: 0.7459\n",
      "has spend time 19m 12s/n\n",
      "val Loss: 0.5484 Acc: 0.7124\n",
      "has spend time 19m 13s/n\n",
      "\n",
      "Epoch 539/9999\n",
      "----------\n",
      "train Loss: 0.5176 Acc: 0.7172\n",
      "has spend time 19m 14s/n\n",
      "val Loss: 0.5433 Acc: 0.7059\n",
      "has spend time 19m 15s/n\n",
      "\n",
      "Epoch 540/9999\n",
      "----------\n",
      "train Loss: 0.5086 Acc: 0.7418\n",
      "has spend time 19m 16s/n\n",
      "val Loss: 0.5628 Acc: 0.6928\n",
      "has spend time 19m 17s/n\n",
      "\n",
      "Epoch 541/9999\n",
      "----------\n",
      "train Loss: 0.4853 Acc: 0.7582\n",
      "has spend time 19m 18s/n\n",
      "val Loss: 0.5523 Acc: 0.6928\n",
      "has spend time 19m 19s/n\n",
      "\n",
      "Epoch 542/9999\n",
      "----------\n",
      "train Loss: 0.4997 Acc: 0.7336\n",
      "has spend time 19m 21s/n\n",
      "val Loss: 0.5472 Acc: 0.7190\n",
      "has spend time 19m 21s/n\n",
      "\n",
      "Epoch 543/9999\n",
      "----------\n",
      "train Loss: 0.5030 Acc: 0.7377\n",
      "has spend time 19m 23s/n\n",
      "val Loss: 0.5444 Acc: 0.7124\n",
      "has spend time 19m 23s/n\n",
      "\n",
      "Epoch 544/9999\n",
      "----------\n",
      "train Loss: 0.4995 Acc: 0.7787\n",
      "has spend time 19m 25s/n\n",
      "val Loss: 0.5495 Acc: 0.7190\n",
      "has spend time 19m 26s/n\n",
      "\n",
      "Epoch 545/9999\n",
      "----------\n",
      "train Loss: 0.4942 Acc: 0.7418\n",
      "has spend time 19m 27s/n\n",
      "val Loss: 0.5492 Acc: 0.7190\n",
      "has spend time 19m 28s/n\n",
      "\n",
      "Epoch 546/9999\n",
      "----------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train Loss: 0.5482 Acc: 0.6967\n",
      "has spend time 19m 29s/n\n",
      "val Loss: 0.5519 Acc: 0.7190\n",
      "has spend time 19m 30s/n\n",
      "\n",
      "Epoch 547/9999\n",
      "----------\n",
      "train Loss: 0.4907 Acc: 0.7705\n",
      "has spend time 19m 31s/n\n",
      "val Loss: 0.5485 Acc: 0.7255\n",
      "has spend time 19m 32s/n\n",
      "\n",
      "Epoch 548/9999\n",
      "----------\n",
      "train Loss: 0.5038 Acc: 0.7172\n",
      "has spend time 19m 33s/n\n",
      "val Loss: 0.5474 Acc: 0.7124\n",
      "has spend time 19m 34s/n\n",
      "\n",
      "Epoch 549/9999\n",
      "----------\n",
      "train Loss: 0.5075 Acc: 0.7459\n",
      "has spend time 19m 35s/n\n",
      "val Loss: 0.5673 Acc: 0.6928\n",
      "has spend time 19m 36s/n\n",
      "\n",
      "Epoch 550/9999\n",
      "----------\n",
      "train Loss: 0.5348 Acc: 0.7377\n",
      "has spend time 19m 37s/n\n",
      "val Loss: 0.5513 Acc: 0.6993\n",
      "has spend time 19m 38s/n\n",
      "\n",
      "Epoch 551/9999\n",
      "----------\n",
      "train Loss: 0.5013 Acc: 0.7336\n",
      "has spend time 19m 40s/n\n",
      "val Loss: 0.5517 Acc: 0.6993\n",
      "has spend time 19m 40s/n\n",
      "\n",
      "Epoch 552/9999\n",
      "----------\n",
      "train Loss: 0.5148 Acc: 0.7459\n",
      "has spend time 19m 42s/n\n",
      "val Loss: 0.5547 Acc: 0.6993\n",
      "has spend time 19m 42s/n\n",
      "\n",
      "Epoch 553/9999\n",
      "----------\n",
      "train Loss: 0.4663 Acc: 0.7869\n",
      "has spend time 19m 44s/n\n",
      "val Loss: 0.5540 Acc: 0.6993\n",
      "has spend time 19m 44s/n\n",
      "\n",
      "Epoch 554/9999\n",
      "----------\n",
      "train Loss: 0.4867 Acc: 0.7746\n",
      "has spend time 19m 46s/n\n",
      "val Loss: 0.5393 Acc: 0.7190\n",
      "has spend time 19m 46s/n\n",
      "\n",
      "Epoch 555/9999\n",
      "----------\n",
      "train Loss: 0.4696 Acc: 0.7582\n",
      "has spend time 19m 48s/n\n",
      "val Loss: 0.5418 Acc: 0.7190\n",
      "has spend time 19m 48s/n\n",
      "\n",
      "Epoch 556/9999\n",
      "----------\n",
      "train Loss: 0.5041 Acc: 0.7295\n",
      "has spend time 19m 50s/n\n",
      "val Loss: 0.5483 Acc: 0.7124\n",
      "has spend time 19m 51s/n\n",
      "\n",
      "Epoch 557/9999\n",
      "----------\n",
      "train Loss: 0.4994 Acc: 0.7500\n",
      "has spend time 19m 52s/n\n",
      "val Loss: 0.5587 Acc: 0.6993\n",
      "has spend time 19m 53s/n\n",
      "\n",
      "Epoch 558/9999\n",
      "----------\n",
      "train Loss: 0.4929 Acc: 0.7500\n",
      "has spend time 19m 54s/n\n",
      "val Loss: 0.5563 Acc: 0.6993\n",
      "has spend time 19m 55s/n\n",
      "\n",
      "Epoch 559/9999\n",
      "----------\n",
      "train Loss: 0.4978 Acc: 0.7582\n",
      "has spend time 19m 56s/n\n",
      "val Loss: 0.5417 Acc: 0.7190\n",
      "has spend time 19m 57s/n\n",
      "\n",
      "Epoch 560/9999\n",
      "----------\n",
      "train Loss: 0.4744 Acc: 0.7664\n",
      "has spend time 19m 58s/n\n",
      "val Loss: 0.5433 Acc: 0.7190\n",
      "has spend time 19m 59s/n\n",
      "\n",
      "Epoch 561/9999\n",
      "----------\n",
      "train Loss: 0.5007 Acc: 0.7582\n",
      "has spend time 20m 0s/n\n",
      "val Loss: 0.5449 Acc: 0.7124\n",
      "has spend time 20m 1s/n\n",
      "\n",
      "Epoch 562/9999\n",
      "----------\n",
      "train Loss: 0.4942 Acc: 0.7418\n",
      "has spend time 20m 3s/n\n",
      "val Loss: 0.5427 Acc: 0.7124\n",
      "has spend time 20m 4s/n\n",
      "\n",
      "Epoch 563/9999\n",
      "----------\n",
      "train Loss: 0.5202 Acc: 0.7664\n",
      "has spend time 20m 5s/n\n",
      "val Loss: 0.5512 Acc: 0.6993\n",
      "has spend time 20m 6s/n\n",
      "\n",
      "Epoch 564/9999\n",
      "----------\n",
      "train Loss: 0.4892 Acc: 0.7582\n",
      "has spend time 20m 7s/n\n",
      "val Loss: 0.5479 Acc: 0.7124\n",
      "has spend time 20m 8s/n\n",
      "\n",
      "Epoch 565/9999\n",
      "----------\n",
      "train Loss: 0.5053 Acc: 0.7582\n",
      "has spend time 20m 9s/n\n",
      "val Loss: 0.5514 Acc: 0.6993\n",
      "has spend time 20m 10s/n\n",
      "\n",
      "Epoch 566/9999\n",
      "----------\n",
      "train Loss: 0.5237 Acc: 0.7131\n",
      "has spend time 20m 11s/n\n",
      "val Loss: 0.5673 Acc: 0.6797\n",
      "has spend time 20m 12s/n\n",
      "\n",
      "Epoch 567/9999\n",
      "----------\n",
      "train Loss: 0.5211 Acc: 0.7090\n",
      "has spend time 20m 13s/n\n",
      "val Loss: 0.5486 Acc: 0.7255\n",
      "has spend time 20m 14s/n\n",
      "\n",
      "Epoch 568/9999\n",
      "----------\n",
      "train Loss: 0.5026 Acc: 0.7541\n",
      "has spend time 20m 15s/n\n",
      "val Loss: 0.5469 Acc: 0.7059\n",
      "has spend time 20m 16s/n\n",
      "\n",
      "Epoch 569/9999\n",
      "----------\n",
      "train Loss: 0.4986 Acc: 0.7500\n",
      "has spend time 20m 18s/n\n",
      "val Loss: 0.5498 Acc: 0.6928\n",
      "has spend time 20m 18s/n\n",
      "\n",
      "Epoch 570/9999\n",
      "----------\n",
      "train Loss: 0.4737 Acc: 0.7418\n",
      "has spend time 20m 20s/n\n",
      "val Loss: 0.5428 Acc: 0.7190\n",
      "has spend time 20m 20s/n\n",
      "\n",
      "Epoch 571/9999\n",
      "----------\n",
      "train Loss: 0.5017 Acc: 0.7418\n",
      "has spend time 20m 22s/n\n",
      "val Loss: 0.5478 Acc: 0.7059\n",
      "has spend time 20m 22s/n\n",
      "\n",
      "Epoch 572/9999\n",
      "----------\n",
      "train Loss: 0.4882 Acc: 0.7418\n",
      "has spend time 20m 24s/n\n",
      "val Loss: 0.5446 Acc: 0.7124\n",
      "has spend time 20m 24s/n\n",
      "\n",
      "Epoch 573/9999\n",
      "----------\n",
      "train Loss: 0.5620 Acc: 0.7213\n",
      "has spend time 20m 26s/n\n",
      "val Loss: 0.5444 Acc: 0.6993\n",
      "has spend time 20m 26s/n\n",
      "\n",
      "Epoch 574/9999\n",
      "----------\n",
      "train Loss: 0.5289 Acc: 0.7295\n",
      "has spend time 20m 28s/n\n",
      "val Loss: 0.5473 Acc: 0.7059\n",
      "has spend time 20m 28s/n\n",
      "\n",
      "Epoch 575/9999\n",
      "----------\n",
      "train Loss: 0.5321 Acc: 0.7377\n",
      "has spend time 20m 30s/n\n",
      "val Loss: 0.5523 Acc: 0.7059\n",
      "has spend time 20m 30s/n\n",
      "\n",
      "Epoch 576/9999\n",
      "----------\n",
      "train Loss: 0.5410 Acc: 0.7131\n",
      "has spend time 20m 32s/n\n",
      "val Loss: 0.5512 Acc: 0.7124\n",
      "has spend time 20m 33s/n\n",
      "\n",
      "Epoch 577/9999\n",
      "----------\n",
      "train Loss: 0.5082 Acc: 0.7418\n",
      "has spend time 20m 34s/n\n",
      "val Loss: 0.5481 Acc: 0.6928\n",
      "has spend time 20m 35s/n\n",
      "\n",
      "Epoch 578/9999\n",
      "----------\n",
      "train Loss: 0.4901 Acc: 0.7705\n",
      "has spend time 20m 36s/n\n",
      "val Loss: 0.5465 Acc: 0.7059\n",
      "has spend time 20m 37s/n\n",
      "\n",
      "Epoch 579/9999\n",
      "----------\n",
      "train Loss: 0.5248 Acc: 0.7336\n",
      "has spend time 20m 39s/n\n",
      "val Loss: 0.5539 Acc: 0.6993\n",
      "has spend time 20m 39s/n\n",
      "\n",
      "Epoch 580/9999\n",
      "----------\n",
      "train Loss: 0.5048 Acc: 0.7623\n",
      "has spend time 20m 41s/n\n",
      "val Loss: 0.5468 Acc: 0.7255\n",
      "has spend time 20m 41s/n\n",
      "\n",
      "Epoch 581/9999\n",
      "----------\n",
      "train Loss: 0.5191 Acc: 0.7459\n",
      "has spend time 20m 43s/n\n",
      "val Loss: 0.5425 Acc: 0.7124\n",
      "has spend time 20m 44s/n\n",
      "\n",
      "Epoch 582/9999\n",
      "----------\n",
      "train Loss: 0.4886 Acc: 0.7459\n",
      "has spend time 20m 45s/n\n",
      "val Loss: 0.5428 Acc: 0.7124\n",
      "has spend time 20m 46s/n\n",
      "\n",
      "Epoch 583/9999\n",
      "----------\n",
      "train Loss: 0.5393 Acc: 0.7172\n",
      "has spend time 20m 47s/n\n",
      "val Loss: 0.5543 Acc: 0.6993\n",
      "has spend time 20m 48s/n\n",
      "\n",
      "Epoch 584/9999\n",
      "----------\n",
      "train Loss: 0.5065 Acc: 0.7254\n",
      "has spend time 20m 49s/n\n",
      "val Loss: 0.5556 Acc: 0.7059\n",
      "has spend time 20m 50s/n\n",
      "\n",
      "Epoch 585/9999\n",
      "----------\n",
      "train Loss: 0.4674 Acc: 0.7746\n",
      "has spend time 20m 51s/n\n",
      "val Loss: 0.5553 Acc: 0.7255\n",
      "has spend time 20m 52s/n\n",
      "\n",
      "Epoch 586/9999\n",
      "----------\n",
      "train Loss: 0.5010 Acc: 0.7582\n",
      "has spend time 20m 53s/n\n",
      "val Loss: 0.5586 Acc: 0.7059\n",
      "has spend time 20m 54s/n\n",
      "\n",
      "Epoch 587/9999\n",
      "----------\n",
      "train Loss: 0.4912 Acc: 0.7582\n",
      "has spend time 20m 55s/n\n",
      "val Loss: 0.5585 Acc: 0.7059\n",
      "has spend time 20m 56s/n\n",
      "\n",
      "Epoch 588/9999\n",
      "----------\n",
      "train Loss: 0.4974 Acc: 0.7746\n",
      "has spend time 20m 57s/n\n",
      "val Loss: 0.5471 Acc: 0.7190\n",
      "has spend time 20m 58s/n\n",
      "\n",
      "Epoch 589/9999\n",
      "----------\n",
      "train Loss: 0.4943 Acc: 0.7541\n",
      "has spend time 20m 60s/n\n",
      "val Loss: 0.5445 Acc: 0.7059\n",
      "has spend time 21m 0s/n\n",
      "\n",
      "Epoch 590/9999\n",
      "----------\n",
      "train Loss: 0.5147 Acc: 0.7090\n",
      "has spend time 21m 2s/n\n",
      "val Loss: 0.5488 Acc: 0.7059\n",
      "has spend time 21m 2s/n\n",
      "\n",
      "Epoch 591/9999\n",
      "----------\n",
      "train Loss: 0.5151 Acc: 0.7418\n",
      "has spend time 21m 4s/n\n",
      "val Loss: 0.5507 Acc: 0.7059\n",
      "has spend time 21m 4s/n\n",
      "\n",
      "Epoch 592/9999\n",
      "----------\n",
      "train Loss: 0.4926 Acc: 0.7459\n",
      "has spend time 21m 6s/n\n",
      "val Loss: 0.5482 Acc: 0.7059\n",
      "has spend time 21m 7s/n\n",
      "\n",
      "Epoch 593/9999\n",
      "----------\n",
      "train Loss: 0.5146 Acc: 0.7500\n",
      "has spend time 21m 8s/n\n",
      "val Loss: 0.5446 Acc: 0.7124\n",
      "has spend time 21m 9s/n\n",
      "\n",
      "Epoch 594/9999\n",
      "----------\n",
      "train Loss: 0.5389 Acc: 0.7090\n",
      "has spend time 21m 10s/n\n",
      "val Loss: 0.5494 Acc: 0.7059\n",
      "has spend time 21m 11s/n\n",
      "\n",
      "Epoch 595/9999\n",
      "----------\n",
      "train Loss: 0.4971 Acc: 0.7828\n",
      "has spend time 21m 13s/n\n",
      "val Loss: 0.5489 Acc: 0.7190\n",
      "has spend time 21m 13s/n\n",
      "\n",
      "Epoch 596/9999\n",
      "----------\n",
      "train Loss: 0.4914 Acc: 0.7582\n",
      "has spend time 21m 15s/n\n",
      "val Loss: 0.5380 Acc: 0.7124\n",
      "has spend time 21m 15s/n\n",
      "\n",
      "Epoch 597/9999\n",
      "----------\n",
      "train Loss: 0.5082 Acc: 0.7623\n",
      "has spend time 21m 17s/n\n",
      "val Loss: 0.5519 Acc: 0.7124\n",
      "has spend time 21m 17s/n\n",
      "\n",
      "Epoch 598/9999\n",
      "----------\n",
      "train Loss: 0.4642 Acc: 0.7705\n",
      "has spend time 21m 19s/n\n",
      "val Loss: 0.5507 Acc: 0.6928\n",
      "has spend time 21m 19s/n\n",
      "\n",
      "Epoch 599/9999\n",
      "----------\n",
      "train Loss: 0.5014 Acc: 0.7131\n",
      "has spend time 21m 21s/n\n",
      "val Loss: 0.5499 Acc: 0.7124\n",
      "has spend time 21m 21s/n\n",
      "\n",
      "Epoch 600/9999\n",
      "----------\n",
      "train Loss: 0.5425 Acc: 0.7131\n",
      "has spend time 21m 23s/n\n",
      "val Loss: 0.5536 Acc: 0.6993\n",
      "has spend time 21m 23s/n\n",
      "\n",
      "Epoch 601/9999\n",
      "----------\n",
      "train Loss: 0.5291 Acc: 0.7377\n",
      "has spend time 21m 25s/n\n",
      "val Loss: 0.5565 Acc: 0.6993\n",
      "has spend time 21m 25s/n\n",
      "\n",
      "Epoch 602/9999\n",
      "----------\n",
      "train Loss: 0.4904 Acc: 0.7172\n",
      "has spend time 21m 27s/n\n",
      "val Loss: 0.5597 Acc: 0.7059\n",
      "has spend time 21m 28s/n\n",
      "\n",
      "Epoch 603/9999\n",
      "----------\n",
      "train Loss: 0.4853 Acc: 0.7541\n",
      "has spend time 21m 29s/n\n",
      "val Loss: 0.5448 Acc: 0.7190\n",
      "has spend time 21m 30s/n\n",
      "\n",
      "Epoch 604/9999\n",
      "----------\n",
      "train Loss: 0.5200 Acc: 0.7213\n",
      "has spend time 21m 31s/n\n",
      "val Loss: 0.5437 Acc: 0.7255\n",
      "has spend time 21m 32s/n\n",
      "\n",
      "Epoch 605/9999\n",
      "----------\n",
      "train Loss: 0.5029 Acc: 0.7336\n",
      "has spend time 21m 33s/n\n",
      "val Loss: 0.5436 Acc: 0.7190\n",
      "has spend time 21m 34s/n\n",
      "\n",
      "Epoch 606/9999\n",
      "----------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train Loss: 0.4915 Acc: 0.7705\n",
      "has spend time 21m 35s/n\n",
      "val Loss: 0.5551 Acc: 0.7124\n",
      "has spend time 21m 36s/n\n",
      "\n",
      "Epoch 607/9999\n",
      "----------\n",
      "train Loss: 0.4944 Acc: 0.7377\n",
      "has spend time 21m 38s/n\n",
      "val Loss: 0.5417 Acc: 0.7059\n",
      "has spend time 21m 38s/n\n",
      "\n",
      "Epoch 608/9999\n",
      "----------\n",
      "train Loss: 0.5057 Acc: 0.7418\n",
      "has spend time 21m 40s/n\n",
      "val Loss: 0.5410 Acc: 0.7255\n",
      "has spend time 21m 40s/n\n",
      "\n",
      "Epoch 609/9999\n",
      "----------\n",
      "train Loss: 0.5217 Acc: 0.7500\n",
      "has spend time 21m 42s/n\n",
      "val Loss: 0.5508 Acc: 0.7059\n",
      "has spend time 21m 43s/n\n",
      "\n",
      "Epoch 610/9999\n",
      "----------\n",
      "train Loss: 0.4535 Acc: 0.7705\n",
      "has spend time 21m 44s/n\n",
      "val Loss: 0.5519 Acc: 0.7059\n",
      "has spend time 21m 45s/n\n",
      "\n",
      "Epoch 611/9999\n",
      "----------\n",
      "train Loss: 0.5015 Acc: 0.7377\n",
      "has spend time 21m 46s/n\n",
      "val Loss: 0.5494 Acc: 0.7124\n",
      "has spend time 21m 47s/n\n",
      "\n",
      "Epoch 612/9999\n",
      "----------\n",
      "train Loss: 0.4769 Acc: 0.7869\n",
      "has spend time 21m 48s/n\n",
      "val Loss: 0.5497 Acc: 0.7255\n",
      "has spend time 21m 49s/n\n",
      "\n",
      "Epoch 613/9999\n",
      "----------\n",
      "train Loss: 0.4731 Acc: 0.7746\n",
      "has spend time 21m 50s/n\n",
      "val Loss: 0.5508 Acc: 0.7190\n",
      "has spend time 21m 51s/n\n",
      "\n",
      "Epoch 614/9999\n",
      "----------\n",
      "train Loss: 0.5025 Acc: 0.7295\n",
      "has spend time 21m 53s/n\n",
      "val Loss: 0.5433 Acc: 0.7059\n",
      "has spend time 21m 54s/n\n",
      "\n",
      "Epoch 615/9999\n",
      "----------\n",
      "train Loss: 0.4854 Acc: 0.7705\n",
      "has spend time 21m 55s/n\n",
      "val Loss: 0.5669 Acc: 0.6993\n",
      "has spend time 21m 56s/n\n",
      "\n",
      "Epoch 616/9999\n",
      "----------\n",
      "train Loss: 0.5184 Acc: 0.7254\n",
      "has spend time 21m 57s/n\n",
      "val Loss: 0.5570 Acc: 0.6993\n",
      "has spend time 21m 58s/n\n",
      "\n",
      "Epoch 617/9999\n",
      "----------\n",
      "train Loss: 0.5065 Acc: 0.7705\n",
      "has spend time 21m 59s/n\n",
      "val Loss: 0.5577 Acc: 0.6993\n",
      "has spend time 21m 60s/n\n",
      "\n",
      "Epoch 618/9999\n",
      "----------\n",
      "train Loss: 0.5078 Acc: 0.7254\n",
      "has spend time 22m 1s/n\n",
      "val Loss: 0.5485 Acc: 0.7190\n",
      "has spend time 22m 2s/n\n",
      "\n",
      "Epoch 619/9999\n",
      "----------\n",
      "train Loss: 0.5199 Acc: 0.7459\n",
      "has spend time 22m 3s/n\n",
      "val Loss: 0.5417 Acc: 0.7190\n",
      "has spend time 22m 4s/n\n",
      "\n",
      "Epoch 620/9999\n",
      "----------\n",
      "train Loss: 0.5066 Acc: 0.7541\n",
      "has spend time 22m 5s/n\n",
      "val Loss: 0.5480 Acc: 0.6993\n",
      "has spend time 22m 6s/n\n",
      "\n",
      "Epoch 621/9999\n",
      "----------\n",
      "train Loss: 0.5285 Acc: 0.7254\n",
      "has spend time 22m 7s/n\n",
      "val Loss: 0.5497 Acc: 0.7124\n",
      "has spend time 22m 8s/n\n",
      "\n",
      "Epoch 622/9999\n",
      "----------\n",
      "train Loss: 0.5047 Acc: 0.7459\n",
      "has spend time 22m 9s/n\n",
      "val Loss: 0.5523 Acc: 0.7124\n",
      "has spend time 22m 10s/n\n",
      "\n",
      "Epoch 623/9999\n",
      "----------\n",
      "train Loss: 0.5212 Acc: 0.7049\n",
      "has spend time 22m 11s/n\n",
      "val Loss: 0.5455 Acc: 0.7124\n",
      "has spend time 22m 12s/n\n",
      "\n",
      "Epoch 624/9999\n",
      "----------\n",
      "train Loss: 0.5082 Acc: 0.7090\n",
      "has spend time 22m 13s/n\n",
      "val Loss: 0.5538 Acc: 0.7255\n",
      "has spend time 22m 14s/n\n",
      "\n",
      "Epoch 625/9999\n",
      "----------\n",
      "train Loss: 0.4763 Acc: 0.7869\n",
      "has spend time 22m 16s/n\n",
      "val Loss: 0.5431 Acc: 0.7124\n",
      "has spend time 22m 16s/n\n",
      "\n",
      "Epoch 626/9999\n",
      "----------\n",
      "train Loss: 0.5056 Acc: 0.7746\n",
      "has spend time 22m 18s/n\n",
      "val Loss: 0.5536 Acc: 0.6863\n",
      "has spend time 22m 18s/n\n",
      "\n",
      "Epoch 627/9999\n",
      "----------\n",
      "train Loss: 0.5245 Acc: 0.7213\n",
      "has spend time 22m 20s/n\n",
      "val Loss: 0.5447 Acc: 0.6993\n",
      "has spend time 22m 21s/n\n",
      "\n",
      "Epoch 628/9999\n",
      "----------\n",
      "train Loss: 0.5030 Acc: 0.7459\n",
      "has spend time 22m 22s/n\n",
      "val Loss: 0.5401 Acc: 0.7124\n",
      "has spend time 22m 23s/n\n",
      "\n",
      "Epoch 629/9999\n",
      "----------\n",
      "train Loss: 0.4963 Acc: 0.7418\n",
      "has spend time 22m 24s/n\n",
      "val Loss: 0.5454 Acc: 0.7190\n",
      "has spend time 22m 25s/n\n",
      "\n",
      "Epoch 630/9999\n",
      "----------\n",
      "train Loss: 0.5035 Acc: 0.7336\n",
      "has spend time 22m 26s/n\n",
      "val Loss: 0.5703 Acc: 0.6993\n",
      "has spend time 22m 27s/n\n",
      "\n",
      "Epoch 631/9999\n",
      "----------\n",
      "train Loss: 0.5222 Acc: 0.7336\n",
      "has spend time 22m 28s/n\n",
      "val Loss: 0.5595 Acc: 0.6993\n",
      "has spend time 22m 29s/n\n",
      "\n",
      "Epoch 632/9999\n",
      "----------\n",
      "train Loss: 0.4907 Acc: 0.7910\n",
      "has spend time 22m 31s/n\n",
      "val Loss: 0.5506 Acc: 0.6993\n",
      "has spend time 22m 31s/n\n",
      "\n",
      "Epoch 633/9999\n",
      "----------\n",
      "train Loss: 0.5048 Acc: 0.7295\n",
      "has spend time 22m 33s/n\n",
      "val Loss: 0.5437 Acc: 0.7124\n",
      "has spend time 22m 34s/n\n",
      "\n",
      "Epoch 634/9999\n",
      "----------\n",
      "train Loss: 0.5294 Acc: 0.6926\n",
      "has spend time 22m 35s/n\n",
      "val Loss: 0.5425 Acc: 0.7255\n",
      "has spend time 22m 36s/n\n",
      "\n",
      "Epoch 635/9999\n",
      "----------\n",
      "train Loss: 0.5024 Acc: 0.7541\n",
      "has spend time 22m 37s/n\n",
      "val Loss: 0.5509 Acc: 0.7059\n",
      "has spend time 22m 38s/n\n",
      "\n",
      "Epoch 636/9999\n",
      "----------\n",
      "train Loss: 0.4951 Acc: 0.7172\n",
      "has spend time 22m 39s/n\n",
      "val Loss: 0.5451 Acc: 0.7124\n",
      "has spend time 22m 40s/n\n",
      "\n",
      "Epoch 637/9999\n",
      "----------\n",
      "train Loss: 0.4926 Acc: 0.7213\n",
      "has spend time 22m 41s/n\n",
      "val Loss: 0.5455 Acc: 0.7124\n",
      "has spend time 22m 42s/n\n",
      "\n",
      "Epoch 638/9999\n",
      "----------\n",
      "train Loss: 0.5194 Acc: 0.7500\n",
      "has spend time 22m 44s/n\n",
      "val Loss: 0.5498 Acc: 0.6993\n",
      "has spend time 22m 44s/n\n",
      "\n",
      "Epoch 639/9999\n",
      "----------\n",
      "train Loss: 0.5127 Acc: 0.7295\n",
      "has spend time 22m 46s/n\n",
      "val Loss: 0.5537 Acc: 0.6863\n",
      "has spend time 22m 46s/n\n",
      "\n",
      "Epoch 640/9999\n",
      "----------\n",
      "train Loss: 0.5299 Acc: 0.7131\n",
      "has spend time 22m 48s/n\n",
      "val Loss: 0.5593 Acc: 0.6993\n",
      "has spend time 22m 48s/n\n",
      "\n",
      "Epoch 641/9999\n",
      "----------\n",
      "train Loss: 0.5324 Acc: 0.7172\n",
      "has spend time 22m 50s/n\n",
      "val Loss: 0.5527 Acc: 0.7059\n",
      "has spend time 22m 50s/n\n",
      "\n",
      "Epoch 642/9999\n",
      "----------\n",
      "train Loss: 0.5265 Acc: 0.7254\n",
      "has spend time 22m 52s/n\n",
      "val Loss: 0.5463 Acc: 0.7190\n",
      "has spend time 22m 53s/n\n",
      "\n",
      "Epoch 643/9999\n",
      "----------\n",
      "train Loss: 0.5111 Acc: 0.7377\n",
      "has spend time 22m 54s/n\n",
      "val Loss: 0.5464 Acc: 0.7190\n",
      "has spend time 22m 55s/n\n",
      "\n",
      "Epoch 644/9999\n",
      "----------\n",
      "train Loss: 0.5200 Acc: 0.7418\n",
      "has spend time 22m 56s/n\n",
      "val Loss: 0.5691 Acc: 0.6863\n",
      "has spend time 22m 57s/n\n",
      "\n",
      "Epoch 645/9999\n",
      "----------\n",
      "train Loss: 0.5087 Acc: 0.7459\n",
      "has spend time 22m 58s/n\n",
      "val Loss: 0.5463 Acc: 0.7124\n",
      "has spend time 22m 59s/n\n",
      "\n",
      "Epoch 646/9999\n",
      "----------\n",
      "train Loss: 0.4890 Acc: 0.7582\n",
      "has spend time 23m 0s/n\n",
      "val Loss: 0.5537 Acc: 0.7059\n",
      "has spend time 23m 1s/n\n",
      "\n",
      "Epoch 647/9999\n",
      "----------\n",
      "train Loss: 0.4874 Acc: 0.7377\n",
      "has spend time 23m 3s/n\n",
      "val Loss: 0.5566 Acc: 0.7059\n",
      "has spend time 23m 3s/n\n",
      "\n",
      "Epoch 648/9999\n",
      "----------\n",
      "train Loss: 0.5215 Acc: 0.7418\n",
      "has spend time 23m 5s/n\n",
      "val Loss: 0.5533 Acc: 0.7059\n",
      "has spend time 23m 5s/n\n",
      "\n",
      "Epoch 649/9999\n",
      "----------\n",
      "train Loss: 0.4990 Acc: 0.7500\n",
      "has spend time 23m 7s/n\n",
      "val Loss: 0.5561 Acc: 0.7124\n",
      "has spend time 23m 8s/n\n",
      "\n",
      "Epoch 650/9999\n",
      "----------\n",
      "train Loss: 0.5263 Acc: 0.7254\n",
      "has spend time 23m 9s/n\n",
      "val Loss: 0.5593 Acc: 0.6993\n",
      "has spend time 23m 10s/n\n",
      "\n",
      "Epoch 651/9999\n",
      "----------\n",
      "train Loss: 0.4799 Acc: 0.7582\n",
      "has spend time 23m 11s/n\n",
      "val Loss: 0.5448 Acc: 0.6928\n",
      "has spend time 23m 12s/n\n",
      "\n",
      "Epoch 652/9999\n",
      "----------\n",
      "train Loss: 0.5150 Acc: 0.7172\n",
      "has spend time 23m 14s/n\n",
      "val Loss: 0.5534 Acc: 0.7124\n",
      "has spend time 23m 14s/n\n",
      "\n",
      "Epoch 653/9999\n",
      "----------\n",
      "train Loss: 0.5098 Acc: 0.7254\n",
      "has spend time 23m 16s/n\n",
      "val Loss: 0.5512 Acc: 0.7124\n",
      "has spend time 23m 16s/n\n",
      "\n",
      "Epoch 654/9999\n",
      "----------\n",
      "train Loss: 0.5290 Acc: 0.7008\n",
      "has spend time 23m 18s/n\n",
      "val Loss: 0.5569 Acc: 0.6928\n",
      "has spend time 23m 18s/n\n",
      "\n",
      "Epoch 655/9999\n",
      "----------\n",
      "train Loss: 0.5288 Acc: 0.6967\n",
      "has spend time 23m 20s/n\n",
      "val Loss: 0.5590 Acc: 0.6863\n",
      "has spend time 23m 20s/n\n",
      "\n",
      "Epoch 656/9999\n",
      "----------\n",
      "train Loss: 0.5212 Acc: 0.7213\n",
      "has spend time 23m 22s/n\n",
      "val Loss: 0.5566 Acc: 0.7059\n",
      "has spend time 23m 22s/n\n",
      "\n",
      "Epoch 657/9999\n",
      "----------\n",
      "train Loss: 0.5382 Acc: 0.7131\n",
      "has spend time 23m 24s/n\n",
      "val Loss: 0.5572 Acc: 0.7059\n",
      "has spend time 23m 24s/n\n",
      "\n",
      "Epoch 658/9999\n",
      "----------\n",
      "train Loss: 0.5463 Acc: 0.6885\n",
      "has spend time 23m 26s/n\n",
      "val Loss: 0.5443 Acc: 0.7124\n",
      "has spend time 23m 26s/n\n",
      "\n",
      "Epoch 659/9999\n",
      "----------\n",
      "train Loss: 0.5108 Acc: 0.7295\n",
      "has spend time 23m 28s/n\n",
      "val Loss: 0.5451 Acc: 0.7255\n",
      "has spend time 23m 28s/n\n",
      "\n",
      "Epoch 660/9999\n",
      "----------\n",
      "train Loss: 0.5451 Acc: 0.7418\n",
      "has spend time 23m 30s/n\n",
      "val Loss: 0.5459 Acc: 0.6993\n",
      "has spend time 23m 31s/n\n",
      "\n",
      "Epoch 661/9999\n",
      "----------\n",
      "train Loss: 0.4807 Acc: 0.7828\n",
      "has spend time 23m 32s/n\n",
      "val Loss: 0.5392 Acc: 0.7190\n",
      "has spend time 23m 33s/n\n",
      "\n",
      "Epoch 662/9999\n",
      "----------\n",
      "train Loss: 0.5089 Acc: 0.7664\n",
      "has spend time 23m 34s/n\n",
      "val Loss: 0.5488 Acc: 0.6993\n",
      "has spend time 23m 35s/n\n",
      "\n",
      "Epoch 663/9999\n",
      "----------\n",
      "train Loss: 0.5179 Acc: 0.7254\n",
      "has spend time 23m 36s/n\n",
      "val Loss: 0.5463 Acc: 0.7190\n",
      "has spend time 23m 37s/n\n",
      "\n",
      "Epoch 664/9999\n",
      "----------\n",
      "train Loss: 0.4990 Acc: 0.7746\n",
      "has spend time 23m 38s/n\n",
      "val Loss: 0.5463 Acc: 0.7255\n",
      "has spend time 23m 39s/n\n",
      "\n",
      "Epoch 665/9999\n",
      "----------\n",
      "train Loss: 0.4900 Acc: 0.7418\n",
      "has spend time 23m 40s/n\n",
      "val Loss: 0.5412 Acc: 0.6993\n",
      "has spend time 23m 41s/n\n",
      "\n",
      "Epoch 666/9999\n",
      "----------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train Loss: 0.5305 Acc: 0.6967\n",
      "has spend time 23m 42s/n\n",
      "val Loss: 0.5427 Acc: 0.7124\n",
      "has spend time 23m 43s/n\n",
      "\n",
      "Epoch 667/9999\n",
      "----------\n",
      "train Loss: 0.4967 Acc: 0.7131\n",
      "has spend time 23m 44s/n\n",
      "val Loss: 0.5428 Acc: 0.7255\n",
      "has spend time 23m 45s/n\n",
      "\n",
      "Epoch 668/9999\n",
      "----------\n",
      "train Loss: 0.5286 Acc: 0.7049\n",
      "has spend time 23m 47s/n\n",
      "val Loss: 0.5475 Acc: 0.7059\n",
      "has spend time 23m 48s/n\n",
      "\n",
      "Epoch 669/9999\n",
      "----------\n",
      "train Loss: 0.5430 Acc: 0.6926\n",
      "has spend time 23m 49s/n\n",
      "val Loss: 0.5526 Acc: 0.7059\n",
      "has spend time 23m 50s/n\n",
      "\n",
      "Epoch 670/9999\n",
      "----------\n",
      "train Loss: 0.5142 Acc: 0.7377\n",
      "has spend time 23m 51s/n\n",
      "val Loss: 0.5426 Acc: 0.7124\n",
      "has spend time 23m 52s/n\n",
      "\n",
      "Epoch 671/9999\n",
      "----------\n",
      "train Loss: 0.4931 Acc: 0.7500\n",
      "has spend time 23m 53s/n\n",
      "val Loss: 0.5764 Acc: 0.6993\n",
      "has spend time 23m 54s/n\n",
      "\n",
      "Epoch 672/9999\n",
      "----------\n",
      "train Loss: 0.5318 Acc: 0.7213\n",
      "has spend time 23m 55s/n\n",
      "val Loss: 0.5575 Acc: 0.6993\n",
      "has spend time 23m 56s/n\n",
      "\n",
      "Epoch 673/9999\n",
      "----------\n",
      "train Loss: 0.4919 Acc: 0.7336\n",
      "has spend time 23m 57s/n\n",
      "val Loss: 0.5521 Acc: 0.7255\n",
      "has spend time 23m 58s/n\n",
      "\n",
      "Epoch 674/9999\n",
      "----------\n",
      "train Loss: 0.5056 Acc: 0.7541\n",
      "has spend time 23m 60s/n\n",
      "val Loss: 0.5424 Acc: 0.7255\n",
      "has spend time 24m 0s/n\n",
      "\n",
      "Epoch 675/9999\n",
      "----------\n",
      "train Loss: 0.4753 Acc: 0.7582\n",
      "has spend time 24m 2s/n\n",
      "val Loss: 0.5478 Acc: 0.6993\n",
      "has spend time 24m 3s/n\n",
      "\n",
      "Epoch 676/9999\n",
      "----------\n",
      "train Loss: 0.5190 Acc: 0.7090\n",
      "has spend time 24m 4s/n\n",
      "val Loss: 0.5475 Acc: 0.7059\n",
      "has spend time 24m 5s/n\n",
      "\n",
      "Epoch 677/9999\n",
      "----------\n",
      "train Loss: 0.5053 Acc: 0.7500\n",
      "has spend time 24m 6s/n\n",
      "val Loss: 0.5401 Acc: 0.7190\n",
      "has spend time 24m 7s/n\n",
      "\n",
      "Epoch 678/9999\n",
      "----------\n",
      "train Loss: 0.5224 Acc: 0.7336\n",
      "has spend time 24m 8s/n\n",
      "val Loss: 0.5567 Acc: 0.6928\n",
      "has spend time 24m 9s/n\n",
      "\n",
      "Epoch 679/9999\n",
      "----------\n",
      "train Loss: 0.5232 Acc: 0.7090\n",
      "has spend time 24m 10s/n\n",
      "val Loss: 0.5545 Acc: 0.7059\n",
      "has spend time 24m 11s/n\n",
      "\n",
      "Epoch 680/9999\n",
      "----------\n",
      "train Loss: 0.5084 Acc: 0.7254\n",
      "has spend time 24m 12s/n\n",
      "val Loss: 0.5501 Acc: 0.7124\n",
      "has spend time 24m 13s/n\n",
      "\n",
      "Epoch 681/9999\n",
      "----------\n",
      "train Loss: 0.5119 Acc: 0.7664\n",
      "has spend time 24m 15s/n\n",
      "val Loss: 0.5681 Acc: 0.6928\n",
      "has spend time 24m 15s/n\n",
      "\n",
      "Epoch 682/9999\n",
      "----------\n",
      "train Loss: 0.5050 Acc: 0.7213\n",
      "has spend time 24m 17s/n\n",
      "val Loss: 0.5549 Acc: 0.6993\n",
      "has spend time 24m 17s/n\n",
      "\n",
      "Epoch 683/9999\n",
      "----------\n",
      "train Loss: 0.5057 Acc: 0.7500\n",
      "has spend time 24m 19s/n\n",
      "val Loss: 0.5528 Acc: 0.6993\n",
      "has spend time 24m 19s/n\n",
      "\n",
      "Epoch 684/9999\n",
      "----------\n",
      "train Loss: 0.5245 Acc: 0.7254\n",
      "has spend time 24m 21s/n\n",
      "val Loss: 0.5503 Acc: 0.7059\n",
      "has spend time 24m 21s/n\n",
      "\n",
      "Epoch 685/9999\n",
      "----------\n",
      "train Loss: 0.4900 Acc: 0.7377\n",
      "has spend time 24m 23s/n\n",
      "val Loss: 0.5417 Acc: 0.7124\n",
      "has spend time 24m 23s/n\n",
      "\n",
      "Epoch 686/9999\n",
      "----------\n",
      "train Loss: 0.5103 Acc: 0.7459\n",
      "has spend time 24m 25s/n\n",
      "val Loss: 0.5444 Acc: 0.7255\n",
      "has spend time 24m 25s/n\n",
      "\n",
      "Epoch 687/9999\n",
      "----------\n",
      "train Loss: 0.5308 Acc: 0.7172\n",
      "has spend time 24m 27s/n\n",
      "val Loss: 0.5568 Acc: 0.7059\n",
      "has spend time 24m 28s/n\n",
      "\n",
      "Epoch 688/9999\n",
      "----------\n",
      "train Loss: 0.5262 Acc: 0.6885\n",
      "has spend time 24m 29s/n\n",
      "val Loss: 0.5521 Acc: 0.7124\n",
      "has spend time 24m 30s/n\n",
      "\n",
      "Epoch 689/9999\n",
      "----------\n",
      "train Loss: 0.5018 Acc: 0.7336\n",
      "has spend time 24m 31s/n\n",
      "val Loss: 0.5508 Acc: 0.7190\n",
      "has spend time 24m 32s/n\n",
      "\n",
      "Epoch 690/9999\n",
      "----------\n",
      "train Loss: 0.5172 Acc: 0.7336\n",
      "has spend time 24m 33s/n\n",
      "val Loss: 0.5601 Acc: 0.6993\n",
      "has spend time 24m 34s/n\n",
      "\n",
      "Epoch 691/9999\n",
      "----------\n",
      "train Loss: 0.5338 Acc: 0.7295\n",
      "has spend time 24m 36s/n\n",
      "val Loss: 0.5546 Acc: 0.7124\n",
      "has spend time 24m 36s/n\n",
      "\n",
      "Epoch 692/9999\n",
      "----------\n",
      "train Loss: 0.4972 Acc: 0.7459\n",
      "has spend time 24m 38s/n\n",
      "val Loss: 0.5596 Acc: 0.7059\n",
      "has spend time 24m 39s/n\n",
      "\n",
      "Epoch 693/9999\n",
      "----------\n",
      "train Loss: 0.4760 Acc: 0.7746\n",
      "has spend time 24m 40s/n\n",
      "val Loss: 0.5512 Acc: 0.7059\n",
      "has spend time 24m 41s/n\n",
      "\n",
      "Epoch 694/9999\n",
      "----------\n",
      "train Loss: 0.5123 Acc: 0.7008\n",
      "has spend time 24m 42s/n\n",
      "val Loss: 0.5551 Acc: 0.7059\n",
      "has spend time 24m 43s/n\n",
      "\n",
      "Epoch 695/9999\n",
      "----------\n",
      "train Loss: 0.5616 Acc: 0.7008\n",
      "has spend time 24m 44s/n\n",
      "val Loss: 0.5468 Acc: 0.7124\n",
      "has spend time 24m 45s/n\n",
      "\n",
      "Epoch 696/9999\n",
      "----------\n",
      "train Loss: 0.5074 Acc: 0.7377\n",
      "has spend time 24m 46s/n\n",
      "val Loss: 0.5478 Acc: 0.7059\n",
      "has spend time 24m 47s/n\n",
      "\n",
      "Epoch 697/9999\n",
      "----------\n",
      "train Loss: 0.5006 Acc: 0.7213\n",
      "has spend time 24m 48s/n\n",
      "val Loss: 0.5726 Acc: 0.6928\n",
      "has spend time 24m 49s/n\n",
      "\n",
      "Epoch 698/9999\n",
      "----------\n",
      "train Loss: 0.5523 Acc: 0.7008\n",
      "has spend time 24m 51s/n\n",
      "val Loss: 0.5423 Acc: 0.7190\n",
      "has spend time 24m 51s/n\n",
      "\n",
      "Epoch 699/9999\n",
      "----------\n",
      "train Loss: 0.5146 Acc: 0.7254\n",
      "has spend time 24m 53s/n\n",
      "val Loss: 0.5539 Acc: 0.7124\n",
      "has spend time 24m 54s/n\n",
      "\n",
      "Epoch 700/9999\n",
      "----------\n",
      "train Loss: 0.5092 Acc: 0.7459\n",
      "has spend time 24m 55s/n\n",
      "val Loss: 0.5552 Acc: 0.7059\n",
      "has spend time 24m 56s/n\n",
      "\n",
      "Epoch 701/9999\n",
      "----------\n",
      "train Loss: 0.5075 Acc: 0.7254\n",
      "has spend time 24m 57s/n\n",
      "val Loss: 0.5426 Acc: 0.7124\n",
      "has spend time 24m 58s/n\n",
      "\n",
      "Epoch 702/9999\n",
      "----------\n",
      "train Loss: 0.5074 Acc: 0.7582\n",
      "has spend time 24m 59s/n\n",
      "val Loss: 0.5341 Acc: 0.7190\n",
      "has spend time 24m 60s/n\n",
      "\n",
      "Epoch 703/9999\n",
      "----------\n",
      "train Loss: 0.5158 Acc: 0.7459\n",
      "has spend time 25m 1s/n\n",
      "val Loss: 0.5380 Acc: 0.7124\n",
      "has spend time 25m 2s/n\n",
      "\n",
      "Epoch 704/9999\n",
      "----------\n",
      "train Loss: 0.5196 Acc: 0.7336\n",
      "has spend time 25m 3s/n\n",
      "val Loss: 0.5520 Acc: 0.6928\n",
      "has spend time 25m 4s/n\n",
      "\n",
      "Epoch 705/9999\n",
      "----------\n",
      "train Loss: 0.5098 Acc: 0.7336\n",
      "has spend time 25m 6s/n\n",
      "val Loss: 0.5464 Acc: 0.7190\n",
      "has spend time 25m 7s/n\n",
      "\n",
      "Epoch 706/9999\n",
      "----------\n",
      "train Loss: 0.4876 Acc: 0.7623\n",
      "has spend time 25m 8s/n\n",
      "val Loss: 0.5450 Acc: 0.7255\n",
      "has spend time 25m 9s/n\n",
      "\n",
      "Epoch 707/9999\n",
      "----------\n",
      "train Loss: 0.5274 Acc: 0.7213\n",
      "has spend time 25m 10s/n\n",
      "val Loss: 0.5473 Acc: 0.7255\n",
      "has spend time 25m 11s/n\n",
      "\n",
      "Epoch 708/9999\n",
      "----------\n",
      "train Loss: 0.5112 Acc: 0.7500\n",
      "has spend time 25m 12s/n\n",
      "val Loss: 0.5518 Acc: 0.6993\n",
      "has spend time 25m 13s/n\n",
      "\n",
      "Epoch 709/9999\n",
      "----------\n",
      "train Loss: 0.4975 Acc: 0.7541\n",
      "has spend time 25m 15s/n\n",
      "val Loss: 0.5603 Acc: 0.6928\n",
      "has spend time 25m 15s/n\n",
      "\n",
      "Epoch 710/9999\n",
      "----------\n",
      "train Loss: 0.5320 Acc: 0.7213\n",
      "has spend time 25m 17s/n\n",
      "val Loss: 0.5433 Acc: 0.7124\n",
      "has spend time 25m 17s/n\n",
      "\n",
      "Epoch 711/9999\n",
      "----------\n",
      "train Loss: 0.4936 Acc: 0.7623\n",
      "has spend time 25m 19s/n\n",
      "val Loss: 0.5534 Acc: 0.7059\n",
      "has spend time 25m 19s/n\n",
      "\n",
      "Epoch 712/9999\n",
      "----------\n",
      "train Loss: 0.4837 Acc: 0.7541\n",
      "has spend time 25m 21s/n\n",
      "val Loss: 0.5557 Acc: 0.6928\n",
      "has spend time 25m 21s/n\n",
      "\n",
      "Epoch 713/9999\n",
      "----------\n",
      "train Loss: 0.4979 Acc: 0.7295\n",
      "has spend time 25m 23s/n\n",
      "val Loss: 0.5433 Acc: 0.7320\n",
      "has spend time 25m 24s/n\n",
      "\n",
      "Epoch 714/9999\n",
      "----------\n",
      "train Loss: 0.5120 Acc: 0.7623\n",
      "has spend time 25m 25s/n\n",
      "val Loss: 0.5685 Acc: 0.7059\n",
      "has spend time 25m 26s/n\n",
      "\n",
      "Epoch 715/9999\n",
      "----------\n",
      "train Loss: 0.5013 Acc: 0.7541\n",
      "has spend time 25m 28s/n\n",
      "val Loss: 0.5515 Acc: 0.6993\n",
      "has spend time 25m 28s/n\n",
      "\n",
      "Epoch 716/9999\n",
      "----------\n",
      "train Loss: 0.5407 Acc: 0.6967\n",
      "has spend time 25m 30s/n\n",
      "val Loss: 0.5503 Acc: 0.6928\n",
      "has spend time 25m 30s/n\n",
      "\n",
      "Epoch 717/9999\n",
      "----------\n",
      "train Loss: 0.4940 Acc: 0.7459\n",
      "has spend time 25m 32s/n\n",
      "val Loss: 0.5510 Acc: 0.7124\n",
      "has spend time 25m 33s/n\n",
      "\n",
      "Epoch 718/9999\n",
      "----------\n",
      "train Loss: 0.5150 Acc: 0.7418\n",
      "has spend time 25m 34s/n\n",
      "val Loss: 0.5483 Acc: 0.6993\n",
      "has spend time 25m 35s/n\n",
      "\n",
      "Epoch 719/9999\n",
      "----------\n",
      "train Loss: 0.5258 Acc: 0.7213\n",
      "has spend time 25m 36s/n\n",
      "val Loss: 0.5454 Acc: 0.7255\n",
      "has spend time 25m 37s/n\n",
      "\n",
      "Epoch 720/9999\n",
      "----------\n",
      "train Loss: 0.5002 Acc: 0.7459\n",
      "has spend time 25m 38s/n\n",
      "val Loss: 0.5452 Acc: 0.7190\n",
      "has spend time 25m 39s/n\n",
      "\n",
      "Epoch 721/9999\n",
      "----------\n",
      "train Loss: 0.5022 Acc: 0.7541\n",
      "has spend time 25m 41s/n\n",
      "val Loss: 0.5414 Acc: 0.7190\n",
      "has spend time 25m 41s/n\n",
      "\n",
      "Epoch 722/9999\n",
      "----------\n",
      "train Loss: 0.5195 Acc: 0.7459\n",
      "has spend time 25m 43s/n\n",
      "val Loss: 0.5504 Acc: 0.7124\n",
      "has spend time 25m 43s/n\n",
      "\n",
      "Epoch 723/9999\n",
      "----------\n",
      "train Loss: 0.5104 Acc: 0.7172\n",
      "has spend time 25m 45s/n\n",
      "val Loss: 0.5504 Acc: 0.7059\n",
      "has spend time 25m 45s/n\n",
      "\n",
      "Epoch 724/9999\n",
      "----------\n",
      "train Loss: 0.4907 Acc: 0.7541\n",
      "has spend time 25m 47s/n\n",
      "val Loss: 0.5564 Acc: 0.6993\n",
      "has spend time 25m 47s/n\n",
      "\n",
      "Epoch 725/9999\n",
      "----------\n",
      "train Loss: 0.5064 Acc: 0.7295\n",
      "has spend time 25m 49s/n\n",
      "val Loss: 0.5486 Acc: 0.7255\n",
      "has spend time 25m 50s/n\n",
      "\n",
      "Epoch 726/9999\n",
      "----------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train Loss: 0.5146 Acc: 0.7377\n",
      "has spend time 25m 51s/n\n",
      "val Loss: 0.5607 Acc: 0.6993\n",
      "has spend time 25m 52s/n\n",
      "\n",
      "Epoch 727/9999\n",
      "----------\n",
      "train Loss: 0.4910 Acc: 0.7828\n",
      "has spend time 25m 53s/n\n",
      "val Loss: 0.5531 Acc: 0.7059\n",
      "has spend time 25m 54s/n\n",
      "\n",
      "Epoch 728/9999\n",
      "----------\n",
      "train Loss: 0.4790 Acc: 0.7746\n",
      "has spend time 25m 55s/n\n",
      "val Loss: 0.5435 Acc: 0.7124\n",
      "has spend time 25m 56s/n\n",
      "\n",
      "Epoch 729/9999\n",
      "----------\n",
      "train Loss: 0.5192 Acc: 0.7500\n",
      "has spend time 25m 57s/n\n",
      "val Loss: 0.5470 Acc: 0.7124\n",
      "has spend time 25m 58s/n\n",
      "\n",
      "Epoch 730/9999\n",
      "----------\n",
      "train Loss: 0.4913 Acc: 0.7623\n",
      "has spend time 25m 60s/n\n",
      "val Loss: 0.5430 Acc: 0.7059\n",
      "has spend time 26m 1s/n\n",
      "\n",
      "Epoch 731/9999\n",
      "----------\n",
      "train Loss: 0.4966 Acc: 0.7377\n",
      "has spend time 26m 2s/n\n",
      "val Loss: 0.5463 Acc: 0.7124\n",
      "has spend time 26m 3s/n\n",
      "\n",
      "Epoch 732/9999\n",
      "----------\n",
      "train Loss: 0.4711 Acc: 0.7787\n",
      "has spend time 26m 5s/n\n",
      "val Loss: 0.5539 Acc: 0.6863\n",
      "has spend time 26m 5s/n\n",
      "\n",
      "Epoch 733/9999\n",
      "----------\n",
      "train Loss: 0.5318 Acc: 0.7172\n",
      "has spend time 26m 7s/n\n",
      "val Loss: 0.5423 Acc: 0.7255\n",
      "has spend time 26m 7s/n\n",
      "\n",
      "Epoch 734/9999\n",
      "----------\n",
      "train Loss: 0.4866 Acc: 0.7500\n",
      "has spend time 26m 9s/n\n",
      "val Loss: 0.5503 Acc: 0.6993\n",
      "has spend time 26m 9s/n\n",
      "\n",
      "Epoch 735/9999\n",
      "----------\n",
      "train Loss: 0.5107 Acc: 0.7336\n",
      "has spend time 26m 11s/n\n",
      "val Loss: 0.5496 Acc: 0.7059\n",
      "has spend time 26m 12s/n\n",
      "\n",
      "Epoch 736/9999\n",
      "----------\n",
      "train Loss: 0.5190 Acc: 0.7172\n",
      "has spend time 26m 13s/n\n",
      "val Loss: 0.5449 Acc: 0.7190\n",
      "has spend time 26m 14s/n\n",
      "\n",
      "Epoch 737/9999\n",
      "----------\n",
      "train Loss: 0.5632 Acc: 0.7172\n",
      "has spend time 26m 15s/n\n",
      "val Loss: 0.5497 Acc: 0.7190\n",
      "has spend time 26m 16s/n\n",
      "\n",
      "Epoch 738/9999\n",
      "----------\n",
      "train Loss: 0.5024 Acc: 0.7254\n",
      "has spend time 26m 17s/n\n",
      "val Loss: 0.5754 Acc: 0.6993\n",
      "has spend time 26m 18s/n\n",
      "\n",
      "Epoch 739/9999\n",
      "----------\n",
      "train Loss: 0.5258 Acc: 0.7295\n",
      "has spend time 26m 19s/n\n",
      "val Loss: 0.5554 Acc: 0.7190\n",
      "has spend time 26m 20s/n\n",
      "\n",
      "Epoch 740/9999\n",
      "----------\n",
      "train Loss: 0.4806 Acc: 0.7459\n",
      "has spend time 26m 21s/n\n",
      "val Loss: 0.5610 Acc: 0.6993\n",
      "has spend time 26m 22s/n\n",
      "\n",
      "Epoch 741/9999\n",
      "----------\n",
      "train Loss: 0.4719 Acc: 0.8115\n",
      "has spend time 26m 24s/n\n",
      "val Loss: 0.5488 Acc: 0.6993\n",
      "has spend time 26m 24s/n\n",
      "\n",
      "Epoch 742/9999\n",
      "----------\n",
      "train Loss: 0.4795 Acc: 0.7828\n",
      "has spend time 26m 26s/n\n",
      "val Loss: 0.5427 Acc: 0.7190\n",
      "has spend time 26m 26s/n\n",
      "\n",
      "Epoch 743/9999\n",
      "----------\n",
      "train Loss: 0.5054 Acc: 0.7295\n",
      "has spend time 26m 28s/n\n",
      "val Loss: 0.5478 Acc: 0.7190\n",
      "has spend time 26m 28s/n\n",
      "\n",
      "Epoch 744/9999\n",
      "----------\n",
      "train Loss: 0.5206 Acc: 0.7295\n",
      "has spend time 26m 30s/n\n",
      "val Loss: 0.5480 Acc: 0.7190\n",
      "has spend time 26m 30s/n\n",
      "\n",
      "Epoch 745/9999\n",
      "----------\n",
      "train Loss: 0.5437 Acc: 0.7049\n",
      "has spend time 26m 32s/n\n",
      "val Loss: 0.5364 Acc: 0.7190\n",
      "has spend time 26m 33s/n\n",
      "\n",
      "Epoch 746/9999\n",
      "----------\n",
      "train Loss: 0.4925 Acc: 0.7623\n",
      "has spend time 26m 34s/n\n",
      "val Loss: 0.5471 Acc: 0.7190\n",
      "has spend time 26m 35s/n\n",
      "\n",
      "Epoch 747/9999\n",
      "----------\n",
      "train Loss: 0.4972 Acc: 0.7541\n",
      "has spend time 26m 36s/n\n",
      "val Loss: 0.5456 Acc: 0.7124\n",
      "has spend time 26m 37s/n\n",
      "\n",
      "Epoch 748/9999\n",
      "----------\n",
      "train Loss: 0.4823 Acc: 0.7418\n",
      "has spend time 26m 38s/n\n",
      "val Loss: 0.5475 Acc: 0.7190\n",
      "has spend time 26m 39s/n\n",
      "\n",
      "Epoch 749/9999\n",
      "----------\n",
      "train Loss: 0.5244 Acc: 0.7418\n",
      "has spend time 26m 40s/n\n",
      "val Loss: 0.5515 Acc: 0.7124\n",
      "has spend time 26m 41s/n\n",
      "\n",
      "Epoch 750/9999\n",
      "----------\n",
      "train Loss: 0.5152 Acc: 0.7623\n",
      "has spend time 26m 42s/n\n",
      "val Loss: 0.5531 Acc: 0.7124\n",
      "has spend time 26m 43s/n\n",
      "\n",
      "Epoch 751/9999\n",
      "----------\n",
      "train Loss: 0.4965 Acc: 0.7623\n",
      "has spend time 26m 44s/n\n",
      "val Loss: 0.5495 Acc: 0.7124\n",
      "has spend time 26m 45s/n\n",
      "\n",
      "Epoch 752/9999\n",
      "----------\n",
      "train Loss: 0.5111 Acc: 0.7418\n",
      "has spend time 26m 46s/n\n",
      "val Loss: 0.5540 Acc: 0.7124\n",
      "has spend time 26m 47s/n\n",
      "\n",
      "Epoch 753/9999\n",
      "----------\n",
      "train Loss: 0.4729 Acc: 0.7664\n",
      "has spend time 26m 49s/n\n",
      "val Loss: 0.5493 Acc: 0.7255\n",
      "has spend time 26m 49s/n\n",
      "\n",
      "Epoch 754/9999\n",
      "----------\n",
      "train Loss: 0.5050 Acc: 0.7541\n",
      "has spend time 26m 51s/n\n",
      "val Loss: 0.5483 Acc: 0.7124\n",
      "has spend time 26m 51s/n\n",
      "\n",
      "Epoch 755/9999\n",
      "----------\n",
      "train Loss: 0.5063 Acc: 0.7172\n",
      "has spend time 26m 53s/n\n",
      "val Loss: 0.5710 Acc: 0.6928\n",
      "has spend time 26m 54s/n\n",
      "\n",
      "Epoch 756/9999\n",
      "----------\n",
      "train Loss: 0.5176 Acc: 0.7418\n",
      "has spend time 26m 55s/n\n",
      "val Loss: 0.5538 Acc: 0.6928\n",
      "has spend time 26m 56s/n\n",
      "\n",
      "Epoch 757/9999\n",
      "----------\n",
      "train Loss: 0.5120 Acc: 0.7418\n",
      "has spend time 26m 57s/n\n",
      "val Loss: 0.5473 Acc: 0.7124\n",
      "has spend time 26m 58s/n\n",
      "\n",
      "Epoch 758/9999\n",
      "----------\n",
      "train Loss: 0.5008 Acc: 0.7541\n",
      "has spend time 26m 60s/n\n",
      "val Loss: 0.5495 Acc: 0.6993\n",
      "has spend time 27m 0s/n\n",
      "\n",
      "Epoch 759/9999\n",
      "----------\n",
      "train Loss: 0.5205 Acc: 0.7254\n",
      "has spend time 27m 2s/n\n",
      "val Loss: 0.5466 Acc: 0.7059\n",
      "has spend time 27m 3s/n\n",
      "\n",
      "Epoch 760/9999\n",
      "----------\n",
      "train Loss: 0.5062 Acc: 0.7459\n",
      "has spend time 27m 5s/n\n",
      "val Loss: 0.5505 Acc: 0.7059\n",
      "has spend time 27m 5s/n\n",
      "\n",
      "Epoch 761/9999\n",
      "----------\n",
      "train Loss: 0.5324 Acc: 0.7418\n",
      "has spend time 27m 7s/n\n",
      "val Loss: 0.5420 Acc: 0.7124\n",
      "has spend time 27m 7s/n\n",
      "\n",
      "Epoch 762/9999\n",
      "----------\n",
      "train Loss: 0.5050 Acc: 0.7541\n",
      "has spend time 27m 9s/n\n",
      "val Loss: 0.5492 Acc: 0.7124\n",
      "has spend time 27m 9s/n\n",
      "\n",
      "Epoch 763/9999\n",
      "----------\n",
      "train Loss: 0.5282 Acc: 0.7172\n",
      "has spend time 27m 11s/n\n",
      "val Loss: 0.5674 Acc: 0.6993\n",
      "has spend time 27m 12s/n\n",
      "\n",
      "Epoch 764/9999\n",
      "----------\n",
      "train Loss: 0.4842 Acc: 0.7746\n",
      "has spend time 27m 13s/n\n",
      "val Loss: 0.5500 Acc: 0.7124\n",
      "has spend time 27m 14s/n\n",
      "\n",
      "Epoch 765/9999\n",
      "----------\n",
      "train Loss: 0.4904 Acc: 0.7705\n",
      "has spend time 27m 15s/n\n",
      "val Loss: 0.5537 Acc: 0.6993\n",
      "has spend time 27m 16s/n\n",
      "\n",
      "Epoch 766/9999\n",
      "----------\n",
      "train Loss: 0.5246 Acc: 0.7500\n",
      "has spend time 27m 17s/n\n",
      "val Loss: 0.5721 Acc: 0.6993\n",
      "has spend time 27m 18s/n\n",
      "\n",
      "Epoch 767/9999\n",
      "----------\n",
      "train Loss: 0.5006 Acc: 0.7582\n",
      "has spend time 27m 20s/n\n",
      "val Loss: 0.5538 Acc: 0.6993\n",
      "has spend time 27m 20s/n\n",
      "\n",
      "Epoch 768/9999\n",
      "----------\n",
      "train Loss: 0.4978 Acc: 0.7459\n",
      "has spend time 27m 22s/n\n",
      "val Loss: 0.5549 Acc: 0.7124\n",
      "has spend time 27m 22s/n\n",
      "\n",
      "Epoch 769/9999\n",
      "----------\n",
      "train Loss: 0.5304 Acc: 0.7377\n",
      "has spend time 27m 24s/n\n",
      "val Loss: 0.5588 Acc: 0.7059\n",
      "has spend time 27m 25s/n\n",
      "\n",
      "Epoch 770/9999\n",
      "----------\n",
      "train Loss: 0.5190 Acc: 0.7377\n",
      "has spend time 27m 26s/n\n",
      "val Loss: 0.5433 Acc: 0.7190\n",
      "has spend time 27m 27s/n\n",
      "\n",
      "Epoch 771/9999\n",
      "----------\n",
      "train Loss: 0.4823 Acc: 0.7828\n",
      "has spend time 27m 28s/n\n",
      "val Loss: 0.5522 Acc: 0.7059\n",
      "has spend time 27m 29s/n\n",
      "\n",
      "Epoch 772/9999\n",
      "----------\n",
      "train Loss: 0.5146 Acc: 0.7500\n",
      "has spend time 27m 30s/n\n",
      "val Loss: 0.5483 Acc: 0.7124\n",
      "has spend time 27m 31s/n\n",
      "\n",
      "Epoch 773/9999\n",
      "----------\n",
      "train Loss: 0.4844 Acc: 0.7664\n",
      "has spend time 27m 33s/n\n",
      "val Loss: 0.5500 Acc: 0.7124\n",
      "has spend time 27m 33s/n\n",
      "\n",
      "Epoch 774/9999\n",
      "----------\n",
      "train Loss: 0.4810 Acc: 0.7418\n",
      "has spend time 27m 35s/n\n",
      "val Loss: 0.5544 Acc: 0.7059\n",
      "has spend time 27m 35s/n\n",
      "\n",
      "Epoch 775/9999\n",
      "----------\n",
      "train Loss: 0.4731 Acc: 0.7746\n",
      "has spend time 27m 37s/n\n",
      "val Loss: 0.5446 Acc: 0.7190\n",
      "has spend time 27m 37s/n\n",
      "\n",
      "Epoch 776/9999\n",
      "----------\n",
      "train Loss: 0.5206 Acc: 0.7541\n",
      "has spend time 27m 39s/n\n",
      "val Loss: 0.5433 Acc: 0.7124\n",
      "has spend time 27m 40s/n\n",
      "\n",
      "Epoch 777/9999\n",
      "----------\n",
      "train Loss: 0.4933 Acc: 0.7582\n",
      "has spend time 27m 41s/n\n",
      "val Loss: 0.5460 Acc: 0.7124\n",
      "has spend time 27m 42s/n\n",
      "\n",
      "Epoch 778/9999\n",
      "----------\n",
      "train Loss: 0.5276 Acc: 0.7500\n",
      "has spend time 27m 43s/n\n",
      "val Loss: 0.5513 Acc: 0.7190\n",
      "has spend time 27m 44s/n\n",
      "\n",
      "Epoch 779/9999\n",
      "----------\n",
      "train Loss: 0.5061 Acc: 0.7582\n",
      "has spend time 27m 45s/n\n",
      "val Loss: 0.5558 Acc: 0.6993\n",
      "has spend time 27m 46s/n\n",
      "\n",
      "Epoch 780/9999\n",
      "----------\n",
      "train Loss: 0.4994 Acc: 0.7500\n",
      "has spend time 27m 48s/n\n",
      "val Loss: 0.5488 Acc: 0.7190\n",
      "has spend time 27m 48s/n\n",
      "\n",
      "Epoch 781/9999\n",
      "----------\n",
      "train Loss: 0.4782 Acc: 0.7541\n",
      "has spend time 27m 50s/n\n",
      "val Loss: 0.5696 Acc: 0.6993\n",
      "has spend time 27m 51s/n\n",
      "\n",
      "Epoch 782/9999\n",
      "----------\n",
      "train Loss: 0.5142 Acc: 0.7295\n",
      "has spend time 27m 52s/n\n",
      "val Loss: 0.5590 Acc: 0.7124\n",
      "has spend time 27m 53s/n\n",
      "\n",
      "Epoch 783/9999\n",
      "----------\n",
      "train Loss: 0.5061 Acc: 0.7377\n",
      "has spend time 27m 54s/n\n",
      "val Loss: 0.5666 Acc: 0.6928\n",
      "has spend time 27m 55s/n\n",
      "\n",
      "Epoch 784/9999\n",
      "----------\n",
      "train Loss: 0.5234 Acc: 0.7254\n",
      "has spend time 27m 56s/n\n",
      "val Loss: 0.5551 Acc: 0.6993\n",
      "has spend time 27m 57s/n\n",
      "\n",
      "Epoch 785/9999\n",
      "----------\n",
      "train Loss: 0.4962 Acc: 0.7746\n",
      "has spend time 27m 58s/n\n",
      "val Loss: 0.5529 Acc: 0.6993\n",
      "has spend time 27m 59s/n\n",
      "\n",
      "Epoch 786/9999\n",
      "----------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train Loss: 0.5282 Acc: 0.7049\n",
      "has spend time 28m 1s/n\n",
      "val Loss: 0.5541 Acc: 0.7059\n",
      "has spend time 28m 1s/n\n",
      "\n",
      "Epoch 787/9999\n",
      "----------\n",
      "train Loss: 0.5133 Acc: 0.7623\n",
      "has spend time 28m 3s/n\n",
      "val Loss: 0.5472 Acc: 0.7190\n",
      "has spend time 28m 3s/n\n",
      "\n",
      "Epoch 788/9999\n",
      "----------\n",
      "train Loss: 0.4702 Acc: 0.7623\n",
      "has spend time 28m 5s/n\n",
      "val Loss: 0.5340 Acc: 0.7255\n",
      "has spend time 28m 5s/n\n",
      "\n",
      "Epoch 789/9999\n",
      "----------\n",
      "train Loss: 0.5216 Acc: 0.7623\n",
      "has spend time 28m 7s/n\n",
      "val Loss: 0.5424 Acc: 0.7190\n",
      "has spend time 28m 7s/n\n",
      "\n",
      "Epoch 790/9999\n",
      "----------\n",
      "train Loss: 0.5100 Acc: 0.7172\n",
      "has spend time 28m 9s/n\n",
      "val Loss: 0.5513 Acc: 0.7059\n",
      "has spend time 28m 10s/n\n",
      "\n",
      "Epoch 791/9999\n",
      "----------\n",
      "train Loss: 0.5023 Acc: 0.7213\n",
      "has spend time 28m 11s/n\n",
      "val Loss: 0.5591 Acc: 0.7059\n",
      "has spend time 28m 12s/n\n",
      "\n",
      "Epoch 792/9999\n",
      "----------\n",
      "train Loss: 0.5245 Acc: 0.7377\n",
      "has spend time 28m 13s/n\n",
      "val Loss: 0.5521 Acc: 0.6993\n",
      "has spend time 28m 14s/n\n",
      "\n",
      "Epoch 793/9999\n",
      "----------\n",
      "train Loss: 0.5208 Acc: 0.7541\n",
      "has spend time 28m 15s/n\n",
      "val Loss: 0.5524 Acc: 0.7190\n",
      "has spend time 28m 16s/n\n",
      "\n",
      "Epoch 794/9999\n",
      "----------\n",
      "train Loss: 0.5101 Acc: 0.7336\n",
      "has spend time 28m 17s/n\n",
      "val Loss: 0.5462 Acc: 0.6993\n",
      "has spend time 28m 18s/n\n",
      "\n",
      "Epoch 795/9999\n",
      "----------\n",
      "train Loss: 0.5284 Acc: 0.7295\n",
      "has spend time 28m 20s/n\n",
      "val Loss: 0.5523 Acc: 0.6993\n",
      "has spend time 28m 20s/n\n",
      "\n",
      "Epoch 796/9999\n",
      "----------\n",
      "train Loss: 0.5035 Acc: 0.7295\n",
      "has spend time 28m 22s/n\n",
      "val Loss: 0.5544 Acc: 0.7059\n",
      "has spend time 28m 22s/n\n",
      "\n",
      "Epoch 797/9999\n",
      "----------\n",
      "train Loss: 0.5226 Acc: 0.7131\n",
      "has spend time 28m 24s/n\n",
      "val Loss: 0.5432 Acc: 0.7124\n",
      "has spend time 28m 24s/n\n",
      "\n",
      "Epoch 798/9999\n",
      "----------\n",
      "train Loss: 0.5132 Acc: 0.7172\n",
      "has spend time 28m 26s/n\n",
      "val Loss: 0.5743 Acc: 0.6993\n",
      "has spend time 28m 26s/n\n",
      "\n",
      "Epoch 799/9999\n",
      "----------\n",
      "train Loss: 0.5183 Acc: 0.7336\n",
      "has spend time 28m 28s/n\n",
      "val Loss: 0.5566 Acc: 0.6928\n",
      "has spend time 28m 28s/n\n",
      "\n",
      "Epoch 800/9999\n",
      "----------\n",
      "train Loss: 0.4721 Acc: 0.7910\n",
      "has spend time 28m 30s/n\n",
      "val Loss: 0.5612 Acc: 0.7059\n",
      "has spend time 28m 31s/n\n",
      "\n",
      "Epoch 801/9999\n",
      "----------\n",
      "train Loss: 0.4904 Acc: 0.7336\n",
      "has spend time 28m 32s/n\n",
      "val Loss: 0.5586 Acc: 0.7124\n",
      "has spend time 28m 33s/n\n",
      "\n",
      "Epoch 802/9999\n",
      "----------\n",
      "train Loss: 0.4940 Acc: 0.7295\n",
      "has spend time 28m 34s/n\n",
      "val Loss: 0.5439 Acc: 0.7190\n",
      "has spend time 28m 35s/n\n",
      "\n",
      "Epoch 803/9999\n",
      "----------\n",
      "train Loss: 0.5034 Acc: 0.7418\n",
      "has spend time 28m 36s/n\n",
      "val Loss: 0.5488 Acc: 0.7124\n",
      "has spend time 28m 37s/n\n",
      "\n",
      "Epoch 804/9999\n",
      "----------\n",
      "train Loss: 0.5300 Acc: 0.7131\n",
      "has spend time 28m 38s/n\n",
      "val Loss: 0.5550 Acc: 0.6928\n",
      "has spend time 28m 39s/n\n",
      "\n",
      "Epoch 805/9999\n",
      "----------\n",
      "train Loss: 0.5172 Acc: 0.7541\n",
      "has spend time 28m 40s/n\n",
      "val Loss: 0.5579 Acc: 0.7059\n",
      "has spend time 28m 41s/n\n",
      "\n",
      "Epoch 806/9999\n",
      "----------\n",
      "train Loss: 0.5109 Acc: 0.7459\n",
      "has spend time 28m 43s/n\n",
      "val Loss: 0.5542 Acc: 0.6993\n",
      "has spend time 28m 43s/n\n",
      "\n",
      "Epoch 807/9999\n",
      "----------\n",
      "train Loss: 0.5340 Acc: 0.7008\n",
      "has spend time 28m 45s/n\n",
      "val Loss: 0.5438 Acc: 0.7190\n",
      "has spend time 28m 45s/n\n",
      "\n",
      "Epoch 808/9999\n",
      "----------\n",
      "train Loss: 0.4830 Acc: 0.7664\n",
      "has spend time 28m 47s/n\n",
      "val Loss: 0.5542 Acc: 0.7059\n",
      "has spend time 28m 48s/n\n",
      "\n",
      "Epoch 809/9999\n",
      "----------\n",
      "train Loss: 0.4844 Acc: 0.7295\n",
      "has spend time 28m 49s/n\n",
      "val Loss: 0.5627 Acc: 0.7059\n",
      "has spend time 28m 50s/n\n",
      "\n",
      "Epoch 810/9999\n",
      "----------\n",
      "train Loss: 0.5170 Acc: 0.7295\n",
      "has spend time 28m 52s/n\n",
      "val Loss: 0.5768 Acc: 0.6928\n",
      "has spend time 28m 52s/n\n",
      "\n",
      "Epoch 811/9999\n",
      "----------\n",
      "train Loss: 0.5678 Acc: 0.7131\n",
      "has spend time 28m 54s/n\n",
      "val Loss: 0.5528 Acc: 0.7059\n",
      "has spend time 28m 55s/n\n",
      "\n",
      "Epoch 812/9999\n",
      "----------\n",
      "train Loss: 0.5098 Acc: 0.7295\n",
      "has spend time 28m 56s/n\n",
      "val Loss: 0.5503 Acc: 0.6993\n",
      "has spend time 28m 57s/n\n",
      "\n",
      "Epoch 813/9999\n",
      "----------\n",
      "train Loss: 0.5219 Acc: 0.7377\n",
      "has spend time 28m 58s/n\n",
      "val Loss: 0.5710 Acc: 0.6928\n",
      "has spend time 28m 59s/n\n",
      "\n",
      "Epoch 814/9999\n",
      "----------\n",
      "train Loss: 0.4828 Acc: 0.7541\n",
      "has spend time 29m 0s/n\n",
      "val Loss: 0.5573 Acc: 0.7059\n",
      "has spend time 29m 1s/n\n",
      "\n",
      "Epoch 815/9999\n",
      "----------\n",
      "train Loss: 0.4964 Acc: 0.7172\n",
      "has spend time 29m 2s/n\n",
      "val Loss: 0.5498 Acc: 0.6993\n",
      "has spend time 29m 3s/n\n",
      "\n",
      "Epoch 816/9999\n",
      "----------\n",
      "train Loss: 0.4902 Acc: 0.7787\n",
      "has spend time 29m 5s/n\n",
      "val Loss: 0.5561 Acc: 0.7059\n",
      "has spend time 29m 5s/n\n",
      "\n",
      "Epoch 817/9999\n",
      "----------\n",
      "train Loss: 0.4830 Acc: 0.7582\n",
      "has spend time 29m 7s/n\n",
      "val Loss: 0.5574 Acc: 0.6928\n",
      "has spend time 29m 8s/n\n",
      "\n",
      "Epoch 818/9999\n",
      "----------\n",
      "train Loss: 0.4753 Acc: 0.7705\n",
      "has spend time 29m 10s/n\n",
      "val Loss: 0.5540 Acc: 0.6993\n",
      "has spend time 29m 10s/n\n",
      "\n",
      "Epoch 819/9999\n",
      "----------\n",
      "train Loss: 0.4962 Acc: 0.7582\n",
      "has spend time 29m 12s/n\n",
      "val Loss: 0.5421 Acc: 0.7124\n",
      "has spend time 29m 12s/n\n",
      "\n",
      "Epoch 820/9999\n",
      "----------\n",
      "train Loss: 0.5256 Acc: 0.7008\n",
      "has spend time 29m 14s/n\n",
      "val Loss: 0.5502 Acc: 0.7059\n",
      "has spend time 29m 14s/n\n",
      "\n",
      "Epoch 821/9999\n",
      "----------\n",
      "train Loss: 0.4688 Acc: 0.7582\n",
      "has spend time 29m 16s/n\n",
      "val Loss: 0.5466 Acc: 0.7124\n",
      "has spend time 29m 16s/n\n",
      "\n",
      "Epoch 822/9999\n",
      "----------\n",
      "train Loss: 0.4957 Acc: 0.7500\n",
      "has spend time 29m 18s/n\n",
      "val Loss: 0.5455 Acc: 0.7255\n",
      "has spend time 29m 19s/n\n",
      "\n",
      "Epoch 823/9999\n",
      "----------\n",
      "train Loss: 0.5255 Acc: 0.7172\n",
      "has spend time 29m 20s/n\n",
      "val Loss: 0.5447 Acc: 0.7255\n",
      "has spend time 29m 21s/n\n",
      "\n",
      "Epoch 824/9999\n",
      "----------\n",
      "train Loss: 0.5129 Acc: 0.7213\n",
      "has spend time 29m 22s/n\n",
      "val Loss: 0.5563 Acc: 0.6928\n",
      "has spend time 29m 23s/n\n",
      "\n",
      "Epoch 825/9999\n",
      "----------\n",
      "train Loss: 0.5221 Acc: 0.7336\n",
      "has spend time 29m 25s/n\n",
      "val Loss: 0.5511 Acc: 0.7190\n",
      "has spend time 29m 25s/n\n",
      "\n",
      "Epoch 826/9999\n",
      "----------\n",
      "train Loss: 0.4988 Acc: 0.7623\n",
      "has spend time 29m 27s/n\n",
      "val Loss: 0.5493 Acc: 0.7190\n",
      "has spend time 29m 27s/n\n",
      "\n",
      "Epoch 827/9999\n",
      "----------\n",
      "train Loss: 0.4981 Acc: 0.7459\n",
      "has spend time 29m 29s/n\n",
      "val Loss: 0.5662 Acc: 0.6993\n",
      "has spend time 29m 30s/n\n",
      "\n",
      "Epoch 828/9999\n",
      "----------\n",
      "train Loss: 0.4971 Acc: 0.7336\n",
      "has spend time 29m 31s/n\n",
      "val Loss: 0.5792 Acc: 0.7059\n",
      "has spend time 29m 32s/n\n",
      "\n",
      "Epoch 829/9999\n",
      "----------\n",
      "train Loss: 0.5252 Acc: 0.7008\n",
      "has spend time 29m 33s/n\n",
      "val Loss: 0.5525 Acc: 0.7059\n",
      "has spend time 29m 34s/n\n",
      "\n",
      "Epoch 830/9999\n",
      "----------\n",
      "train Loss: 0.4973 Acc: 0.7746\n",
      "has spend time 29m 35s/n\n",
      "val Loss: 0.5649 Acc: 0.6993\n",
      "has spend time 29m 36s/n\n",
      "\n",
      "Epoch 831/9999\n",
      "----------\n",
      "train Loss: 0.5002 Acc: 0.7541\n",
      "has spend time 29m 37s/n\n",
      "val Loss: 0.5465 Acc: 0.7124\n",
      "has spend time 29m 38s/n\n",
      "\n",
      "Epoch 832/9999\n",
      "----------\n",
      "train Loss: 0.5277 Acc: 0.6844\n",
      "has spend time 29m 40s/n\n",
      "val Loss: 0.5636 Acc: 0.6993\n",
      "has spend time 29m 41s/n\n",
      "\n",
      "Epoch 833/9999\n",
      "----------\n",
      "train Loss: 0.4852 Acc: 0.7500\n",
      "has spend time 29m 42s/n\n",
      "val Loss: 0.5450 Acc: 0.7190\n",
      "has spend time 29m 43s/n\n",
      "\n",
      "Epoch 834/9999\n",
      "----------\n",
      "train Loss: 0.5123 Acc: 0.7459\n",
      "has spend time 29m 45s/n\n",
      "val Loss: 0.5472 Acc: 0.7124\n",
      "has spend time 29m 45s/n\n",
      "\n",
      "Epoch 835/9999\n",
      "----------\n",
      "train Loss: 0.5281 Acc: 0.7459\n",
      "has spend time 29m 47s/n\n",
      "val Loss: 0.5465 Acc: 0.7255\n",
      "has spend time 29m 47s/n\n",
      "\n",
      "Epoch 836/9999\n",
      "----------\n",
      "train Loss: 0.4742 Acc: 0.7787\n",
      "has spend time 29m 49s/n\n",
      "val Loss: 0.5444 Acc: 0.7190\n",
      "has spend time 29m 50s/n\n",
      "\n",
      "Epoch 837/9999\n",
      "----------\n",
      "train Loss: 0.5128 Acc: 0.7418\n",
      "has spend time 29m 51s/n\n",
      "val Loss: 0.5432 Acc: 0.7255\n",
      "has spend time 29m 52s/n\n",
      "\n",
      "Epoch 838/9999\n",
      "----------\n",
      "train Loss: 0.4781 Acc: 0.7541\n",
      "has spend time 29m 53s/n\n",
      "val Loss: 0.5407 Acc: 0.7124\n",
      "has spend time 29m 54s/n\n",
      "\n",
      "Epoch 839/9999\n",
      "----------\n",
      "train Loss: 0.4873 Acc: 0.7541\n",
      "has spend time 29m 56s/n\n",
      "val Loss: 0.5561 Acc: 0.7059\n",
      "has spend time 29m 56s/n\n",
      "\n",
      "Epoch 840/9999\n",
      "----------\n",
      "train Loss: 0.4959 Acc: 0.7541\n",
      "has spend time 29m 58s/n\n",
      "val Loss: 0.5486 Acc: 0.7124\n",
      "has spend time 29m 58s/n\n",
      "\n",
      "Epoch 841/9999\n",
      "----------\n",
      "train Loss: 0.4751 Acc: 0.7541\n",
      "has spend time 29m 60s/n\n",
      "val Loss: 0.5449 Acc: 0.7124\n",
      "has spend time 30m 1s/n\n",
      "\n",
      "Epoch 842/9999\n",
      "----------\n",
      "train Loss: 0.4967 Acc: 0.7541\n",
      "has spend time 30m 2s/n\n",
      "val Loss: 0.5546 Acc: 0.7190\n",
      "has spend time 30m 3s/n\n",
      "\n",
      "Epoch 843/9999\n",
      "----------\n",
      "train Loss: 0.5044 Acc: 0.7459\n",
      "has spend time 30m 4s/n\n",
      "val Loss: 0.5449 Acc: 0.7190\n",
      "has spend time 30m 5s/n\n",
      "\n",
      "Epoch 844/9999\n",
      "----------\n",
      "train Loss: 0.5026 Acc: 0.7664\n",
      "has spend time 30m 6s/n\n",
      "val Loss: 0.5496 Acc: 0.7124\n",
      "has spend time 30m 7s/n\n",
      "\n",
      "Epoch 845/9999\n",
      "----------\n",
      "train Loss: 0.4889 Acc: 0.7582\n",
      "has spend time 30m 8s/n\n",
      "val Loss: 0.5551 Acc: 0.6993\n",
      "has spend time 30m 9s/n\n",
      "\n",
      "Epoch 846/9999\n",
      "----------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train Loss: 0.4974 Acc: 0.7336\n",
      "has spend time 30m 11s/n\n",
      "val Loss: 0.5576 Acc: 0.6993\n",
      "has spend time 30m 11s/n\n",
      "\n",
      "Epoch 847/9999\n",
      "----------\n",
      "train Loss: 0.5190 Acc: 0.7459\n",
      "has spend time 30m 13s/n\n",
      "val Loss: 0.5588 Acc: 0.6993\n",
      "has spend time 30m 14s/n\n",
      "\n",
      "Epoch 848/9999\n",
      "----------\n",
      "train Loss: 0.5216 Acc: 0.7541\n",
      "has spend time 30m 15s/n\n",
      "val Loss: 0.5495 Acc: 0.7059\n",
      "has spend time 30m 16s/n\n",
      "\n",
      "Epoch 849/9999\n",
      "----------\n",
      "train Loss: 0.5335 Acc: 0.7090\n",
      "has spend time 30m 18s/n\n",
      "val Loss: 0.5505 Acc: 0.7059\n",
      "has spend time 30m 18s/n\n",
      "\n",
      "Epoch 850/9999\n",
      "----------\n",
      "train Loss: 0.5135 Acc: 0.7336\n",
      "has spend time 30m 20s/n\n",
      "val Loss: 0.5404 Acc: 0.7190\n",
      "has spend time 30m 20s/n\n",
      "\n",
      "Epoch 851/9999\n",
      "----------\n",
      "train Loss: 0.5178 Acc: 0.7418\n",
      "has spend time 30m 22s/n\n",
      "val Loss: 0.5524 Acc: 0.6928\n",
      "has spend time 30m 23s/n\n",
      "\n",
      "Epoch 852/9999\n",
      "----------\n",
      "train Loss: 0.4908 Acc: 0.7336\n",
      "has spend time 30m 24s/n\n",
      "val Loss: 0.5500 Acc: 0.7255\n",
      "has spend time 30m 25s/n\n",
      "\n",
      "Epoch 853/9999\n",
      "----------\n",
      "train Loss: 0.4978 Acc: 0.7459\n",
      "has spend time 30m 27s/n\n",
      "val Loss: 0.5580 Acc: 0.6993\n",
      "has spend time 30m 27s/n\n",
      "\n",
      "Epoch 854/9999\n",
      "----------\n",
      "train Loss: 0.5126 Acc: 0.7459\n",
      "has spend time 30m 29s/n\n",
      "val Loss: 0.5655 Acc: 0.6993\n",
      "has spend time 30m 29s/n\n",
      "\n",
      "Epoch 855/9999\n",
      "----------\n",
      "train Loss: 0.4769 Acc: 0.7705\n",
      "has spend time 30m 31s/n\n",
      "val Loss: 0.5547 Acc: 0.6863\n",
      "has spend time 30m 31s/n\n",
      "\n",
      "Epoch 856/9999\n",
      "----------\n",
      "train Loss: 0.5274 Acc: 0.7254\n",
      "has spend time 30m 33s/n\n",
      "val Loss: 0.5395 Acc: 0.7059\n",
      "has spend time 30m 33s/n\n",
      "\n",
      "Epoch 857/9999\n",
      "----------\n",
      "train Loss: 0.4901 Acc: 0.7541\n",
      "has spend time 30m 35s/n\n",
      "val Loss: 0.5410 Acc: 0.7124\n",
      "has spend time 30m 36s/n\n",
      "\n",
      "Epoch 858/9999\n",
      "----------\n",
      "train Loss: 0.4924 Acc: 0.7377\n",
      "has spend time 30m 37s/n\n",
      "val Loss: 0.5481 Acc: 0.7190\n",
      "has spend time 30m 38s/n\n",
      "\n",
      "Epoch 859/9999\n",
      "----------\n",
      "train Loss: 0.5044 Acc: 0.7541\n",
      "has spend time 30m 39s/n\n",
      "val Loss: 0.5411 Acc: 0.7190\n",
      "has spend time 30m 40s/n\n",
      "\n",
      "Epoch 860/9999\n",
      "----------\n",
      "train Loss: 0.5119 Acc: 0.7172\n",
      "has spend time 30m 41s/n\n",
      "val Loss: 0.5598 Acc: 0.6993\n",
      "has spend time 30m 42s/n\n",
      "\n",
      "Epoch 861/9999\n",
      "----------\n",
      "train Loss: 0.5227 Acc: 0.7418\n",
      "has spend time 30m 43s/n\n",
      "val Loss: 0.5451 Acc: 0.7124\n",
      "has spend time 30m 44s/n\n",
      "\n",
      "Epoch 862/9999\n",
      "----------\n",
      "train Loss: 0.4873 Acc: 0.7500\n",
      "has spend time 30m 46s/n\n",
      "val Loss: 0.5541 Acc: 0.6993\n",
      "has spend time 30m 46s/n\n",
      "\n",
      "Epoch 863/9999\n",
      "----------\n",
      "train Loss: 0.4923 Acc: 0.7623\n",
      "has spend time 30m 48s/n\n",
      "val Loss: 0.5480 Acc: 0.7124\n",
      "has spend time 30m 48s/n\n",
      "\n",
      "Epoch 864/9999\n",
      "----------\n",
      "train Loss: 0.5066 Acc: 0.7131\n",
      "has spend time 30m 50s/n\n",
      "val Loss: 0.5458 Acc: 0.7124\n",
      "has spend time 30m 51s/n\n",
      "\n",
      "Epoch 865/9999\n",
      "----------\n",
      "train Loss: 0.5276 Acc: 0.6967\n",
      "has spend time 30m 52s/n\n",
      "val Loss: 0.5465 Acc: 0.7255\n",
      "has spend time 30m 53s/n\n",
      "\n",
      "Epoch 866/9999\n",
      "----------\n",
      "train Loss: 0.4821 Acc: 0.7582\n",
      "has spend time 30m 54s/n\n",
      "val Loss: 0.5589 Acc: 0.6928\n",
      "has spend time 30m 55s/n\n",
      "\n",
      "Epoch 867/9999\n",
      "----------\n",
      "train Loss: 0.5123 Acc: 0.7213\n",
      "has spend time 30m 57s/n\n",
      "val Loss: 0.5467 Acc: 0.6993\n",
      "has spend time 30m 57s/n\n",
      "\n",
      "Epoch 868/9999\n",
      "----------\n",
      "train Loss: 0.4682 Acc: 0.7787\n",
      "has spend time 30m 59s/n\n",
      "val Loss: 0.5635 Acc: 0.7059\n",
      "has spend time 30m 59s/n\n",
      "\n",
      "Epoch 869/9999\n",
      "----------\n",
      "train Loss: 0.5111 Acc: 0.7336\n",
      "has spend time 31m 1s/n\n",
      "val Loss: 0.5683 Acc: 0.6928\n",
      "has spend time 31m 1s/n\n",
      "\n",
      "Epoch 870/9999\n",
      "----------\n",
      "train Loss: 0.5125 Acc: 0.7623\n",
      "has spend time 31m 3s/n\n",
      "val Loss: 0.5565 Acc: 0.6993\n",
      "has spend time 31m 3s/n\n",
      "\n",
      "Epoch 871/9999\n",
      "----------\n",
      "train Loss: 0.5007 Acc: 0.7623\n",
      "has spend time 31m 5s/n\n",
      "val Loss: 0.5506 Acc: 0.7124\n",
      "has spend time 31m 5s/n\n",
      "\n",
      "Epoch 872/9999\n",
      "----------\n",
      "train Loss: 0.5050 Acc: 0.7172\n",
      "has spend time 31m 7s/n\n",
      "val Loss: 0.5484 Acc: 0.7124\n",
      "has spend time 31m 7s/n\n",
      "\n",
      "Epoch 873/9999\n",
      "----------\n",
      "train Loss: 0.4821 Acc: 0.7828\n",
      "has spend time 31m 9s/n\n",
      "val Loss: 0.5539 Acc: 0.6928\n",
      "has spend time 31m 10s/n\n",
      "\n",
      "Epoch 874/9999\n",
      "----------\n",
      "train Loss: 0.4928 Acc: 0.7582\n",
      "has spend time 31m 11s/n\n",
      "val Loss: 0.5551 Acc: 0.7124\n",
      "has spend time 31m 12s/n\n",
      "\n",
      "Epoch 875/9999\n",
      "----------\n",
      "train Loss: 0.5239 Acc: 0.7213\n",
      "has spend time 31m 13s/n\n",
      "val Loss: 0.5567 Acc: 0.7059\n",
      "has spend time 31m 14s/n\n",
      "\n",
      "Epoch 876/9999\n",
      "----------\n",
      "train Loss: 0.5188 Acc: 0.7295\n",
      "has spend time 31m 15s/n\n",
      "val Loss: 0.5603 Acc: 0.6863\n",
      "has spend time 31m 16s/n\n",
      "\n",
      "Epoch 877/9999\n",
      "----------\n",
      "train Loss: 0.5095 Acc: 0.7377\n",
      "has spend time 31m 17s/n\n",
      "val Loss: 0.5584 Acc: 0.7059\n",
      "has spend time 31m 18s/n\n",
      "\n",
      "Epoch 878/9999\n",
      "----------\n",
      "train Loss: 0.4954 Acc: 0.7541\n",
      "has spend time 31m 19s/n\n",
      "val Loss: 0.5410 Acc: 0.7059\n",
      "has spend time 31m 20s/n\n",
      "\n",
      "Epoch 879/9999\n",
      "----------\n",
      "train Loss: 0.5305 Acc: 0.7418\n",
      "has spend time 31m 22s/n\n",
      "val Loss: 0.5459 Acc: 0.6993\n",
      "has spend time 31m 22s/n\n",
      "\n",
      "Epoch 880/9999\n",
      "----------\n",
      "train Loss: 0.5179 Acc: 0.7295\n",
      "has spend time 31m 24s/n\n",
      "val Loss: 0.5444 Acc: 0.7255\n",
      "has spend time 31m 24s/n\n",
      "\n",
      "Epoch 881/9999\n",
      "----------\n",
      "train Loss: 0.5234 Acc: 0.7377\n",
      "has spend time 31m 26s/n\n",
      "val Loss: 0.5400 Acc: 0.7190\n",
      "has spend time 31m 27s/n\n",
      "\n",
      "Epoch 882/9999\n",
      "----------\n",
      "train Loss: 0.5192 Acc: 0.7254\n",
      "has spend time 31m 28s/n\n",
      "val Loss: 0.5394 Acc: 0.7124\n",
      "has spend time 31m 29s/n\n",
      "\n",
      "Epoch 883/9999\n",
      "----------\n",
      "train Loss: 0.5115 Acc: 0.7254\n",
      "has spend time 31m 30s/n\n",
      "val Loss: 0.5491 Acc: 0.7059\n",
      "has spend time 31m 31s/n\n",
      "\n",
      "Epoch 884/9999\n",
      "----------\n",
      "train Loss: 0.5286 Acc: 0.7295\n",
      "has spend time 31m 33s/n\n",
      "val Loss: 0.5459 Acc: 0.7059\n",
      "has spend time 31m 33s/n\n",
      "\n",
      "Epoch 885/9999\n",
      "----------\n",
      "train Loss: 0.5023 Acc: 0.7541\n",
      "has spend time 31m 35s/n\n",
      "val Loss: 0.5499 Acc: 0.7059\n",
      "has spend time 31m 36s/n\n",
      "\n",
      "Epoch 886/9999\n",
      "----------\n",
      "train Loss: 0.5255 Acc: 0.7336\n",
      "has spend time 31m 37s/n\n",
      "val Loss: 0.5468 Acc: 0.7124\n",
      "has spend time 31m 38s/n\n",
      "\n",
      "Epoch 887/9999\n",
      "----------\n",
      "train Loss: 0.5488 Acc: 0.7295\n",
      "has spend time 31m 40s/n\n",
      "val Loss: 0.5404 Acc: 0.7190\n",
      "has spend time 31m 40s/n\n",
      "\n",
      "Epoch 888/9999\n",
      "----------\n",
      "train Loss: 0.4973 Acc: 0.7131\n",
      "has spend time 31m 42s/n\n",
      "val Loss: 0.5655 Acc: 0.7059\n",
      "has spend time 31m 42s/n\n",
      "\n",
      "Epoch 889/9999\n",
      "----------\n",
      "train Loss: 0.5461 Acc: 0.6885\n",
      "has spend time 31m 44s/n\n",
      "val Loss: 0.5513 Acc: 0.7124\n",
      "has spend time 31m 44s/n\n",
      "\n",
      "Epoch 890/9999\n",
      "----------\n",
      "train Loss: 0.4986 Acc: 0.7500\n",
      "has spend time 31m 46s/n\n",
      "val Loss: 0.5561 Acc: 0.7059\n",
      "has spend time 31m 46s/n\n",
      "\n",
      "Epoch 891/9999\n",
      "----------\n",
      "train Loss: 0.5039 Acc: 0.7746\n",
      "has spend time 31m 48s/n\n",
      "val Loss: 0.5493 Acc: 0.7124\n",
      "has spend time 31m 49s/n\n",
      "\n",
      "Epoch 892/9999\n",
      "----------\n",
      "train Loss: 0.5052 Acc: 0.7623\n",
      "has spend time 31m 50s/n\n",
      "val Loss: 0.5586 Acc: 0.7190\n",
      "has spend time 31m 51s/n\n",
      "\n",
      "Epoch 893/9999\n",
      "----------\n",
      "train Loss: 0.5261 Acc: 0.7090\n",
      "has spend time 31m 52s/n\n",
      "val Loss: 0.5494 Acc: 0.7059\n",
      "has spend time 31m 53s/n\n",
      "\n",
      "Epoch 894/9999\n",
      "----------\n",
      "train Loss: 0.4869 Acc: 0.7500\n",
      "has spend time 31m 54s/n\n",
      "val Loss: 0.5450 Acc: 0.7124\n",
      "has spend time 31m 55s/n\n",
      "\n",
      "Epoch 895/9999\n",
      "----------\n",
      "train Loss: 0.4965 Acc: 0.7500\n",
      "has spend time 31m 56s/n\n",
      "val Loss: 0.5543 Acc: 0.7124\n",
      "has spend time 31m 57s/n\n",
      "\n",
      "Epoch 896/9999\n",
      "----------\n",
      "train Loss: 0.5023 Acc: 0.7664\n",
      "has spend time 31m 58s/n\n",
      "val Loss: 0.5548 Acc: 0.7059\n",
      "has spend time 31m 59s/n\n",
      "\n",
      "Epoch 897/9999\n",
      "----------\n",
      "train Loss: 0.4816 Acc: 0.7705\n",
      "has spend time 32m 0s/n\n",
      "val Loss: 0.5455 Acc: 0.7190\n",
      "has spend time 32m 1s/n\n",
      "\n",
      "Epoch 898/9999\n",
      "----------\n",
      "train Loss: 0.5481 Acc: 0.7172\n",
      "has spend time 32m 2s/n\n",
      "val Loss: 0.5358 Acc: 0.7059\n",
      "has spend time 32m 3s/n\n",
      "\n",
      "Epoch 899/9999\n",
      "----------\n",
      "train Loss: 0.5007 Acc: 0.7377\n",
      "has spend time 32m 5s/n\n",
      "val Loss: 0.5524 Acc: 0.7059\n",
      "has spend time 32m 5s/n\n",
      "\n",
      "Epoch 900/9999\n",
      "----------\n",
      "train Loss: 0.5020 Acc: 0.7336\n",
      "has spend time 32m 7s/n\n",
      "val Loss: 0.5499 Acc: 0.6993\n",
      "has spend time 32m 7s/n\n",
      "\n",
      "Epoch 901/9999\n",
      "----------\n",
      "train Loss: 0.5216 Acc: 0.7254\n",
      "has spend time 32m 9s/n\n",
      "val Loss: 0.5565 Acc: 0.7059\n",
      "has spend time 32m 10s/n\n",
      "\n",
      "Epoch 902/9999\n",
      "----------\n",
      "train Loss: 0.5203 Acc: 0.7377\n",
      "has spend time 32m 11s/n\n",
      "val Loss: 0.5486 Acc: 0.7059\n",
      "has spend time 32m 12s/n\n",
      "\n",
      "Epoch 903/9999\n",
      "----------\n",
      "train Loss: 0.5356 Acc: 0.7131\n",
      "has spend time 32m 13s/n\n",
      "val Loss: 0.5550 Acc: 0.7059\n",
      "has spend time 32m 14s/n\n",
      "\n",
      "Epoch 904/9999\n",
      "----------\n",
      "train Loss: 0.5047 Acc: 0.7254\n",
      "has spend time 32m 16s/n\n",
      "val Loss: 0.5397 Acc: 0.7190\n",
      "has spend time 32m 16s/n\n",
      "\n",
      "Epoch 905/9999\n",
      "----------\n",
      "train Loss: 0.5083 Acc: 0.7336\n",
      "has spend time 32m 18s/n\n",
      "val Loss: 0.5556 Acc: 0.7124\n",
      "has spend time 32m 18s/n\n",
      "\n",
      "Epoch 906/9999\n",
      "----------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train Loss: 0.5090 Acc: 0.7254\n",
      "has spend time 32m 20s/n\n",
      "val Loss: 0.5485 Acc: 0.7124\n",
      "has spend time 32m 20s/n\n",
      "\n",
      "Epoch 907/9999\n",
      "----------\n",
      "train Loss: 0.4838 Acc: 0.7377\n",
      "has spend time 32m 22s/n\n",
      "val Loss: 0.5443 Acc: 0.7124\n",
      "has spend time 32m 22s/n\n",
      "\n",
      "Epoch 908/9999\n",
      "----------\n",
      "train Loss: 0.5047 Acc: 0.7295\n",
      "has spend time 32m 24s/n\n",
      "val Loss: 0.5492 Acc: 0.7124\n",
      "has spend time 32m 24s/n\n",
      "\n",
      "Epoch 909/9999\n",
      "----------\n",
      "train Loss: 0.4910 Acc: 0.7213\n",
      "has spend time 32m 26s/n\n",
      "val Loss: 0.5605 Acc: 0.6993\n",
      "has spend time 32m 27s/n\n",
      "\n",
      "Epoch 910/9999\n",
      "----------\n",
      "train Loss: 0.4869 Acc: 0.7500\n",
      "has spend time 32m 28s/n\n",
      "val Loss: 0.5509 Acc: 0.6928\n",
      "has spend time 32m 29s/n\n",
      "\n",
      "Epoch 911/9999\n",
      "----------\n",
      "train Loss: 0.4985 Acc: 0.7377\n",
      "has spend time 32m 30s/n\n",
      "val Loss: 0.5442 Acc: 0.7190\n",
      "has spend time 32m 31s/n\n",
      "\n",
      "Epoch 912/9999\n",
      "----------\n",
      "train Loss: 0.5007 Acc: 0.7336\n",
      "has spend time 32m 32s/n\n",
      "val Loss: 0.5685 Acc: 0.6993\n",
      "has spend time 32m 33s/n\n",
      "\n",
      "Epoch 913/9999\n",
      "----------\n",
      "train Loss: 0.5005 Acc: 0.7336\n",
      "has spend time 32m 35s/n\n",
      "val Loss: 0.5544 Acc: 0.7124\n",
      "has spend time 32m 35s/n\n",
      "\n",
      "Epoch 914/9999\n",
      "----------\n",
      "train Loss: 0.4924 Acc: 0.7582\n",
      "has spend time 32m 37s/n\n",
      "val Loss: 0.5538 Acc: 0.7124\n",
      "has spend time 32m 37s/n\n",
      "\n",
      "Epoch 915/9999\n",
      "----------\n",
      "train Loss: 0.5200 Acc: 0.7336\n",
      "has spend time 32m 39s/n\n",
      "val Loss: 0.5518 Acc: 0.7190\n",
      "has spend time 32m 39s/n\n",
      "\n",
      "Epoch 916/9999\n",
      "----------\n",
      "train Loss: 0.4899 Acc: 0.7377\n",
      "has spend time 32m 41s/n\n",
      "val Loss: 0.5502 Acc: 0.6993\n",
      "has spend time 32m 42s/n\n",
      "\n",
      "Epoch 917/9999\n",
      "----------\n",
      "train Loss: 0.5184 Acc: 0.7254\n",
      "has spend time 32m 43s/n\n",
      "val Loss: 0.5654 Acc: 0.6863\n",
      "has spend time 32m 44s/n\n",
      "\n",
      "Epoch 918/9999\n",
      "----------\n",
      "train Loss: 0.5220 Acc: 0.7418\n",
      "has spend time 32m 45s/n\n",
      "val Loss: 0.5540 Acc: 0.6993\n",
      "has spend time 32m 46s/n\n",
      "\n",
      "Epoch 919/9999\n",
      "----------\n",
      "train Loss: 0.4980 Acc: 0.7623\n",
      "has spend time 32m 47s/n\n",
      "val Loss: 0.5467 Acc: 0.7124\n",
      "has spend time 32m 48s/n\n",
      "\n",
      "Epoch 920/9999\n",
      "----------\n",
      "train Loss: 0.5148 Acc: 0.7254\n",
      "has spend time 32m 49s/n\n",
      "val Loss: 0.5551 Acc: 0.7190\n",
      "has spend time 32m 50s/n\n",
      "\n",
      "Epoch 921/9999\n",
      "----------\n",
      "train Loss: 0.5148 Acc: 0.7172\n",
      "has spend time 32m 51s/n\n",
      "val Loss: 0.5560 Acc: 0.7059\n",
      "has spend time 32m 52s/n\n",
      "\n",
      "Epoch 922/9999\n",
      "----------\n",
      "train Loss: 0.4624 Acc: 0.7869\n",
      "has spend time 32m 53s/n\n",
      "val Loss: 0.5507 Acc: 0.7059\n",
      "has spend time 32m 54s/n\n",
      "\n",
      "Epoch 923/9999\n",
      "----------\n",
      "train Loss: 0.4931 Acc: 0.7377\n",
      "has spend time 32m 55s/n\n",
      "val Loss: 0.5403 Acc: 0.7190\n",
      "has spend time 32m 56s/n\n",
      "\n",
      "Epoch 924/9999\n",
      "----------\n",
      "train Loss: 0.4908 Acc: 0.7664\n",
      "has spend time 32m 57s/n\n",
      "val Loss: 0.5544 Acc: 0.6928\n",
      "has spend time 32m 58s/n\n",
      "\n",
      "Epoch 925/9999\n",
      "----------\n",
      "train Loss: 0.4779 Acc: 0.7623\n",
      "has spend time 32m 60s/n\n",
      "val Loss: 0.5636 Acc: 0.6993\n",
      "has spend time 33m 1s/n\n",
      "\n",
      "Epoch 926/9999\n",
      "----------\n",
      "train Loss: 0.4971 Acc: 0.7500\n",
      "has spend time 33m 2s/n\n",
      "val Loss: 0.5659 Acc: 0.6993\n",
      "has spend time 33m 3s/n\n",
      "\n",
      "Epoch 927/9999\n",
      "----------\n",
      "train Loss: 0.4963 Acc: 0.7418\n",
      "has spend time 33m 4s/n\n",
      "val Loss: 0.5606 Acc: 0.6993\n",
      "has spend time 33m 5s/n\n",
      "\n",
      "Epoch 928/9999\n",
      "----------\n",
      "train Loss: 0.5267 Acc: 0.7131\n",
      "has spend time 33m 6s/n\n",
      "val Loss: 0.5440 Acc: 0.7059\n",
      "has spend time 33m 7s/n\n",
      "\n",
      "Epoch 929/9999\n",
      "----------\n",
      "train Loss: 0.5359 Acc: 0.7008\n",
      "has spend time 33m 8s/n\n",
      "val Loss: 0.5397 Acc: 0.7255\n",
      "has spend time 33m 9s/n\n",
      "\n",
      "Epoch 930/9999\n",
      "----------\n",
      "train Loss: 0.5028 Acc: 0.7582\n",
      "has spend time 33m 10s/n\n",
      "val Loss: 0.5537 Acc: 0.6928\n",
      "has spend time 33m 11s/n\n",
      "\n",
      "Epoch 931/9999\n",
      "----------\n",
      "train Loss: 0.5202 Acc: 0.7049\n",
      "has spend time 33m 13s/n\n",
      "val Loss: 0.5650 Acc: 0.6928\n",
      "has spend time 33m 13s/n\n",
      "\n",
      "Epoch 932/9999\n",
      "----------\n",
      "train Loss: 0.5043 Acc: 0.7213\n",
      "has spend time 33m 15s/n\n",
      "val Loss: 0.5581 Acc: 0.7059\n",
      "has spend time 33m 15s/n\n",
      "\n",
      "Epoch 933/9999\n",
      "----------\n",
      "train Loss: 0.5257 Acc: 0.7336\n",
      "has spend time 33m 17s/n\n",
      "val Loss: 0.5501 Acc: 0.6993\n",
      "has spend time 33m 18s/n\n",
      "\n",
      "Epoch 934/9999\n",
      "----------\n",
      "train Loss: 0.5293 Acc: 0.7254\n",
      "has spend time 33m 19s/n\n",
      "val Loss: 0.5479 Acc: 0.7059\n",
      "has spend time 33m 20s/n\n",
      "\n",
      "Epoch 935/9999\n",
      "----------\n",
      "train Loss: 0.5147 Acc: 0.7049\n",
      "has spend time 33m 21s/n\n",
      "val Loss: 0.5502 Acc: 0.7124\n",
      "has spend time 33m 22s/n\n",
      "\n",
      "Epoch 936/9999\n",
      "----------\n",
      "train Loss: 0.4994 Acc: 0.7500\n",
      "has spend time 33m 23s/n\n",
      "val Loss: 0.5492 Acc: 0.7124\n",
      "has spend time 33m 24s/n\n",
      "\n",
      "Epoch 937/9999\n",
      "----------\n",
      "train Loss: 0.5226 Acc: 0.7336\n",
      "has spend time 33m 25s/n\n",
      "val Loss: 0.5487 Acc: 0.7190\n",
      "has spend time 33m 26s/n\n",
      "\n",
      "Epoch 938/9999\n",
      "----------\n",
      "train Loss: 0.4983 Acc: 0.7295\n",
      "has spend time 33m 27s/n\n",
      "val Loss: 0.5460 Acc: 0.7059\n",
      "has spend time 33m 28s/n\n",
      "\n",
      "Epoch 939/9999\n",
      "----------\n",
      "train Loss: 0.5019 Acc: 0.7418\n",
      "has spend time 33m 30s/n\n",
      "val Loss: 0.5521 Acc: 0.6993\n",
      "has spend time 33m 30s/n\n",
      "\n",
      "Epoch 940/9999\n",
      "----------\n",
      "train Loss: 0.5212 Acc: 0.7746\n",
      "has spend time 33m 32s/n\n",
      "val Loss: 0.5529 Acc: 0.7059\n",
      "has spend time 33m 32s/n\n",
      "\n",
      "Epoch 941/9999\n",
      "----------\n",
      "train Loss: 0.5157 Acc: 0.7418\n",
      "has spend time 33m 34s/n\n",
      "val Loss: 0.5434 Acc: 0.7059\n",
      "has spend time 33m 34s/n\n",
      "\n",
      "Epoch 942/9999\n",
      "----------\n",
      "train Loss: 0.4948 Acc: 0.7377\n",
      "has spend time 33m 36s/n\n",
      "val Loss: 0.5434 Acc: 0.7190\n",
      "has spend time 33m 36s/n\n",
      "\n",
      "Epoch 943/9999\n",
      "----------\n",
      "train Loss: 0.5175 Acc: 0.7213\n",
      "has spend time 33m 38s/n\n",
      "val Loss: 0.5535 Acc: 0.6993\n",
      "has spend time 33m 38s/n\n",
      "\n",
      "Epoch 944/9999\n",
      "----------\n",
      "train Loss: 0.5031 Acc: 0.7459\n",
      "has spend time 33m 40s/n\n",
      "val Loss: 0.5473 Acc: 0.7124\n",
      "has spend time 33m 40s/n\n",
      "\n",
      "Epoch 945/9999\n",
      "----------\n",
      "train Loss: 0.4811 Acc: 0.7869\n",
      "has spend time 33m 42s/n\n",
      "val Loss: 0.5403 Acc: 0.7190\n",
      "has spend time 33m 42s/n\n",
      "\n",
      "Epoch 946/9999\n",
      "----------\n",
      "train Loss: 0.5091 Acc: 0.7500\n",
      "has spend time 33m 44s/n\n",
      "val Loss: 0.5464 Acc: 0.7124\n",
      "has spend time 33m 45s/n\n",
      "\n",
      "Epoch 947/9999\n",
      "----------\n",
      "train Loss: 0.5080 Acc: 0.7418\n",
      "has spend time 33m 46s/n\n",
      "val Loss: 0.5558 Acc: 0.7124\n",
      "has spend time 33m 47s/n\n",
      "\n",
      "Epoch 948/9999\n",
      "----------\n",
      "train Loss: 0.4769 Acc: 0.7664\n",
      "has spend time 33m 48s/n\n",
      "val Loss: 0.5472 Acc: 0.7190\n",
      "has spend time 33m 49s/n\n",
      "\n",
      "Epoch 949/9999\n",
      "----------\n",
      "train Loss: 0.5236 Acc: 0.7295\n",
      "has spend time 33m 50s/n\n",
      "val Loss: 0.5606 Acc: 0.6928\n",
      "has spend time 33m 51s/n\n",
      "\n",
      "Epoch 950/9999\n",
      "----------\n",
      "train Loss: 0.5167 Acc: 0.7377\n",
      "has spend time 33m 52s/n\n",
      "val Loss: 0.5693 Acc: 0.6928\n",
      "has spend time 33m 53s/n\n",
      "\n",
      "Epoch 951/9999\n",
      "----------\n",
      "train Loss: 0.5012 Acc: 0.7500\n",
      "has spend time 33m 55s/n\n",
      "val Loss: 0.5476 Acc: 0.7190\n",
      "has spend time 33m 55s/n\n",
      "\n",
      "Epoch 952/9999\n",
      "----------\n",
      "train Loss: 0.4968 Acc: 0.7377\n",
      "has spend time 33m 57s/n\n",
      "val Loss: 0.5506 Acc: 0.7059\n",
      "has spend time 33m 57s/n\n",
      "\n",
      "Epoch 953/9999\n",
      "----------\n",
      "train Loss: 0.5158 Acc: 0.7418\n",
      "has spend time 33m 59s/n\n",
      "val Loss: 0.5525 Acc: 0.7190\n",
      "has spend time 33m 60s/n\n",
      "\n",
      "Epoch 954/9999\n",
      "----------\n",
      "train Loss: 0.5182 Acc: 0.7172\n",
      "has spend time 34m 1s/n\n",
      "val Loss: 0.5484 Acc: 0.7255\n",
      "has spend time 34m 2s/n\n",
      "\n",
      "Epoch 955/9999\n",
      "----------\n",
      "train Loss: 0.4973 Acc: 0.7623\n",
      "has spend time 34m 3s/n\n",
      "val Loss: 0.5597 Acc: 0.6928\n",
      "has spend time 34m 4s/n\n",
      "\n",
      "Epoch 956/9999\n",
      "----------\n",
      "train Loss: 0.4986 Acc: 0.7582\n",
      "has spend time 34m 5s/n\n",
      "val Loss: 0.5479 Acc: 0.7190\n",
      "has spend time 34m 6s/n\n",
      "\n",
      "Epoch 957/9999\n",
      "----------\n",
      "train Loss: 0.5049 Acc: 0.7377\n",
      "has spend time 34m 7s/n\n",
      "val Loss: 0.5526 Acc: 0.6993\n",
      "has spend time 34m 8s/n\n",
      "\n",
      "Epoch 958/9999\n",
      "----------\n",
      "train Loss: 0.4970 Acc: 0.7664\n",
      "has spend time 34m 9s/n\n",
      "val Loss: 0.5499 Acc: 0.7059\n",
      "has spend time 34m 10s/n\n",
      "\n",
      "Epoch 959/9999\n",
      "----------\n",
      "train Loss: 0.5407 Acc: 0.7336\n",
      "has spend time 34m 11s/n\n",
      "val Loss: 0.5513 Acc: 0.7190\n",
      "has spend time 34m 12s/n\n",
      "\n",
      "Epoch 960/9999\n",
      "----------\n",
      "train Loss: 0.5424 Acc: 0.7336\n",
      "has spend time 34m 13s/n\n",
      "val Loss: 0.5428 Acc: 0.7124\n",
      "has spend time 34m 14s/n\n",
      "\n",
      "Epoch 961/9999\n",
      "----------\n",
      "train Loss: 0.5123 Acc: 0.7254\n",
      "has spend time 34m 15s/n\n",
      "val Loss: 0.5556 Acc: 0.6928\n",
      "has spend time 34m 16s/n\n",
      "\n",
      "Epoch 962/9999\n",
      "----------\n",
      "train Loss: 0.5405 Acc: 0.7090\n",
      "has spend time 34m 17s/n\n",
      "val Loss: 0.5472 Acc: 0.7059\n",
      "has spend time 34m 18s/n\n",
      "\n",
      "Epoch 963/9999\n",
      "----------\n",
      "train Loss: 0.4946 Acc: 0.7500\n",
      "has spend time 34m 20s/n\n",
      "val Loss: 0.5444 Acc: 0.7190\n",
      "has spend time 34m 21s/n\n",
      "\n",
      "Epoch 964/9999\n",
      "----------\n",
      "train Loss: 0.5178 Acc: 0.7090\n",
      "has spend time 34m 22s/n\n",
      "val Loss: 0.5553 Acc: 0.7059\n",
      "has spend time 34m 23s/n\n",
      "\n",
      "Epoch 965/9999\n",
      "----------\n",
      "train Loss: 0.5024 Acc: 0.7623\n",
      "has spend time 34m 24s/n\n",
      "val Loss: 0.5608 Acc: 0.6928\n",
      "has spend time 34m 25s/n\n",
      "\n",
      "Epoch 966/9999\n",
      "----------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train Loss: 0.5057 Acc: 0.7459\n",
      "has spend time 34m 26s/n\n",
      "val Loss: 0.5503 Acc: 0.7124\n",
      "has spend time 34m 27s/n\n",
      "\n",
      "Epoch 967/9999\n",
      "----------\n",
      "train Loss: 0.5181 Acc: 0.7172\n",
      "has spend time 34m 29s/n\n",
      "val Loss: 0.5536 Acc: 0.7124\n",
      "has spend time 34m 29s/n\n",
      "\n",
      "Epoch 968/9999\n",
      "----------\n",
      "train Loss: 0.4881 Acc: 0.7500\n",
      "has spend time 34m 31s/n\n",
      "val Loss: 0.5437 Acc: 0.7190\n",
      "has spend time 34m 31s/n\n",
      "\n",
      "Epoch 969/9999\n",
      "----------\n",
      "train Loss: 0.5207 Acc: 0.7295\n",
      "has spend time 34m 33s/n\n",
      "val Loss: 0.5596 Acc: 0.6928\n",
      "has spend time 34m 34s/n\n",
      "\n",
      "Epoch 970/9999\n",
      "----------\n",
      "train Loss: 0.4949 Acc: 0.7336\n",
      "has spend time 34m 35s/n\n",
      "val Loss: 0.5485 Acc: 0.6993\n",
      "has spend time 34m 36s/n\n",
      "\n",
      "Epoch 971/9999\n",
      "----------\n",
      "train Loss: 0.4969 Acc: 0.7459\n",
      "has spend time 34m 37s/n\n",
      "val Loss: 0.5502 Acc: 0.6993\n",
      "has spend time 34m 38s/n\n",
      "\n",
      "Epoch 972/9999\n",
      "----------\n",
      "train Loss: 0.5134 Acc: 0.7254\n",
      "has spend time 34m 39s/n\n",
      "val Loss: 0.5477 Acc: 0.7190\n",
      "has spend time 34m 40s/n\n",
      "\n",
      "Epoch 973/9999\n",
      "----------\n",
      "train Loss: 0.5546 Acc: 0.7213\n",
      "has spend time 34m 42s/n\n",
      "val Loss: 0.5456 Acc: 0.7190\n",
      "has spend time 34m 42s/n\n",
      "\n",
      "Epoch 974/9999\n",
      "----------\n",
      "train Loss: 0.5242 Acc: 0.7295\n",
      "has spend time 34m 44s/n\n",
      "val Loss: 0.5448 Acc: 0.7190\n",
      "has spend time 34m 44s/n\n",
      "\n",
      "Epoch 975/9999\n",
      "----------\n",
      "train Loss: 0.4937 Acc: 0.7705\n",
      "has spend time 34m 46s/n\n",
      "val Loss: 0.5495 Acc: 0.7190\n",
      "has spend time 34m 46s/n\n",
      "\n",
      "Epoch 976/9999\n",
      "----------\n",
      "train Loss: 0.5014 Acc: 0.7295\n",
      "has spend time 34m 48s/n\n",
      "val Loss: 0.5489 Acc: 0.7255\n",
      "has spend time 34m 48s/n\n",
      "\n",
      "Epoch 977/9999\n",
      "----------\n",
      "train Loss: 0.5052 Acc: 0.7295\n",
      "has spend time 34m 50s/n\n",
      "val Loss: 0.5516 Acc: 0.7124\n",
      "has spend time 34m 50s/n\n",
      "\n",
      "Epoch 978/9999\n",
      "----------\n",
      "train Loss: 0.4962 Acc: 0.7418\n",
      "has spend time 34m 52s/n\n",
      "val Loss: 0.5570 Acc: 0.7059\n",
      "has spend time 34m 52s/n\n",
      "\n",
      "Epoch 979/9999\n",
      "----------\n",
      "train Loss: 0.5036 Acc: 0.7254\n",
      "has spend time 34m 54s/n\n",
      "val Loss: 0.5573 Acc: 0.6993\n",
      "has spend time 34m 54s/n\n",
      "\n",
      "Epoch 980/9999\n",
      "----------\n",
      "train Loss: 0.4942 Acc: 0.7500\n",
      "has spend time 34m 56s/n\n",
      "val Loss: 0.5603 Acc: 0.6928\n",
      "has spend time 34m 56s/n\n",
      "\n",
      "Epoch 981/9999\n",
      "----------\n",
      "train Loss: 0.5132 Acc: 0.7336\n",
      "has spend time 34m 58s/n\n",
      "val Loss: 0.5453 Acc: 0.7255\n",
      "has spend time 34m 59s/n\n",
      "\n",
      "Epoch 982/9999\n",
      "----------\n",
      "train Loss: 0.5161 Acc: 0.7787\n",
      "has spend time 35m 0s/n\n",
      "val Loss: 0.5574 Acc: 0.7124\n",
      "has spend time 35m 1s/n\n",
      "\n",
      "Epoch 983/9999\n",
      "----------\n",
      "train Loss: 0.4663 Acc: 0.7787\n",
      "has spend time 35m 3s/n\n",
      "val Loss: 0.5525 Acc: 0.6993\n",
      "has spend time 35m 3s/n\n",
      "\n",
      "Epoch 984/9999\n",
      "----------\n",
      "train Loss: 0.5234 Acc: 0.7500\n",
      "has spend time 35m 5s/n\n",
      "val Loss: 0.5518 Acc: 0.7124\n",
      "has spend time 35m 5s/n\n",
      "\n",
      "Epoch 985/9999\n",
      "----------\n",
      "train Loss: 0.5137 Acc: 0.7336\n",
      "has spend time 35m 7s/n\n",
      "val Loss: 0.5467 Acc: 0.7190\n",
      "has spend time 35m 7s/n\n",
      "\n",
      "Epoch 986/9999\n",
      "----------\n",
      "train Loss: 0.5227 Acc: 0.7090\n",
      "has spend time 35m 9s/n\n",
      "val Loss: 0.5375 Acc: 0.7255\n",
      "has spend time 35m 9s/n\n",
      "\n",
      "Epoch 987/9999\n",
      "----------\n",
      "train Loss: 0.4868 Acc: 0.7705\n",
      "has spend time 35m 11s/n\n",
      "val Loss: 0.5524 Acc: 0.6863\n",
      "has spend time 35m 11s/n\n",
      "\n",
      "Epoch 988/9999\n",
      "----------\n",
      "train Loss: 0.5027 Acc: 0.7623\n",
      "has spend time 35m 13s/n\n",
      "val Loss: 0.5424 Acc: 0.7124\n",
      "has spend time 35m 13s/n\n",
      "\n",
      "Epoch 989/9999\n",
      "----------\n",
      "train Loss: 0.5284 Acc: 0.7377\n",
      "has spend time 35m 15s/n\n",
      "val Loss: 0.5459 Acc: 0.7190\n",
      "has spend time 35m 16s/n\n",
      "\n",
      "Epoch 990/9999\n",
      "----------\n",
      "train Loss: 0.4816 Acc: 0.7582\n",
      "has spend time 35m 17s/n\n",
      "val Loss: 0.5493 Acc: 0.7124\n",
      "has spend time 35m 18s/n\n",
      "\n",
      "Epoch 991/9999\n",
      "----------\n",
      "train Loss: 0.5273 Acc: 0.7049\n",
      "has spend time 35m 19s/n\n",
      "val Loss: 0.5480 Acc: 0.7124\n",
      "has spend time 35m 20s/n\n",
      "\n",
      "Epoch 992/9999\n",
      "----------\n",
      "train Loss: 0.5151 Acc: 0.7377\n",
      "has spend time 35m 21s/n\n",
      "val Loss: 0.5525 Acc: 0.7124\n",
      "has spend time 35m 22s/n\n",
      "\n",
      "Epoch 993/9999\n",
      "----------\n",
      "train Loss: 0.4842 Acc: 0.7336\n",
      "has spend time 35m 23s/n\n",
      "val Loss: 0.5478 Acc: 0.7124\n",
      "has spend time 35m 24s/n\n",
      "\n",
      "Epoch 994/9999\n",
      "----------\n",
      "train Loss: 0.4787 Acc: 0.7705\n",
      "has spend time 35m 25s/n\n",
      "val Loss: 0.5515 Acc: 0.7059\n",
      "has spend time 35m 26s/n\n",
      "\n",
      "Epoch 995/9999\n",
      "----------\n",
      "train Loss: 0.5334 Acc: 0.7090\n",
      "has spend time 35m 27s/n\n",
      "val Loss: 0.5525 Acc: 0.6993\n",
      "has spend time 35m 28s/n\n",
      "\n",
      "Epoch 996/9999\n",
      "----------\n",
      "train Loss: 0.4999 Acc: 0.7582\n",
      "has spend time 35m 30s/n\n",
      "val Loss: 0.5514 Acc: 0.7190\n",
      "has spend time 35m 31s/n\n",
      "\n",
      "Epoch 997/9999\n",
      "----------\n",
      "train Loss: 0.5186 Acc: 0.7213\n",
      "has spend time 35m 32s/n\n",
      "val Loss: 0.5652 Acc: 0.6928\n",
      "has spend time 35m 33s/n\n",
      "\n",
      "Epoch 998/9999\n",
      "----------\n",
      "train Loss: 0.5239 Acc: 0.7377\n",
      "has spend time 35m 34s/n\n",
      "val Loss: 0.5708 Acc: 0.6928\n",
      "has spend time 35m 35s/n\n",
      "\n",
      "Epoch 999/9999\n",
      "----------\n",
      "train Loss: 0.5014 Acc: 0.7377\n",
      "has spend time 35m 36s/n\n",
      "val Loss: 0.5457 Acc: 0.7190\n",
      "has spend time 35m 37s/n\n",
      "\n",
      "Epoch 1000/9999\n",
      "----------\n",
      "train Loss: 0.5349 Acc: 0.7336\n",
      "has spend time 35m 38s/n\n",
      "val Loss: 0.5582 Acc: 0.6928\n",
      "has spend time 35m 39s/n\n",
      "\n",
      "Epoch 1001/9999\n",
      "----------\n",
      "train Loss: 0.4785 Acc: 0.7582\n",
      "has spend time 35m 41s/n\n",
      "val Loss: 0.5470 Acc: 0.7059\n",
      "has spend time 35m 41s/n\n",
      "\n",
      "Epoch 1002/9999\n",
      "----------\n",
      "train Loss: 0.4901 Acc: 0.7664\n",
      "has spend time 35m 43s/n\n",
      "val Loss: 0.5533 Acc: 0.7059\n",
      "has spend time 35m 43s/n\n",
      "\n",
      "Epoch 1003/9999\n",
      "----------\n",
      "train Loss: 0.5121 Acc: 0.7254\n",
      "has spend time 35m 45s/n\n",
      "val Loss: 0.5512 Acc: 0.6928\n",
      "has spend time 35m 45s/n\n",
      "\n",
      "Epoch 1004/9999\n",
      "----------\n",
      "train Loss: 0.5161 Acc: 0.7336\n",
      "has spend time 35m 47s/n\n",
      "val Loss: 0.5632 Acc: 0.7059\n",
      "has spend time 35m 48s/n\n",
      "\n",
      "Epoch 1005/9999\n",
      "----------\n",
      "train Loss: 0.5188 Acc: 0.7213\n",
      "has spend time 35m 49s/n\n",
      "val Loss: 0.5415 Acc: 0.7190\n",
      "has spend time 35m 50s/n\n",
      "\n",
      "Epoch 1006/9999\n",
      "----------\n",
      "train Loss: 0.4886 Acc: 0.7541\n",
      "has spend time 35m 51s/n\n",
      "val Loss: 0.5479 Acc: 0.6993\n",
      "has spend time 35m 52s/n\n",
      "\n",
      "Epoch 1007/9999\n",
      "----------\n",
      "train Loss: 0.5014 Acc: 0.7131\n",
      "has spend time 35m 54s/n\n",
      "val Loss: 0.5446 Acc: 0.7059\n",
      "has spend time 35m 54s/n\n",
      "\n",
      "Epoch 1008/9999\n",
      "----------\n",
      "train Loss: 0.5089 Acc: 0.7623\n",
      "has spend time 35m 56s/n\n",
      "val Loss: 0.5637 Acc: 0.7059\n",
      "has spend time 35m 56s/n\n",
      "\n",
      "Epoch 1009/9999\n",
      "----------\n",
      "train Loss: 0.5228 Acc: 0.7131\n",
      "has spend time 35m 58s/n\n",
      "val Loss: 0.5517 Acc: 0.7059\n",
      "has spend time 35m 58s/n\n",
      "\n",
      "Epoch 1010/9999\n",
      "----------\n",
      "train Loss: 0.4998 Acc: 0.7746\n",
      "has spend time 35m 60s/n\n",
      "val Loss: 0.5479 Acc: 0.7124\n",
      "has spend time 36m 0s/n\n",
      "\n",
      "Epoch 1011/9999\n",
      "----------\n",
      "train Loss: 0.5030 Acc: 0.7254\n",
      "has spend time 36m 2s/n\n",
      "val Loss: 0.5657 Acc: 0.6928\n",
      "has spend time 36m 2s/n\n",
      "\n",
      "Epoch 1012/9999\n",
      "----------\n",
      "train Loss: 0.5053 Acc: 0.7254\n",
      "has spend time 36m 4s/n\n",
      "val Loss: 0.5454 Acc: 0.7124\n",
      "has spend time 36m 4s/n\n",
      "\n",
      "Epoch 1013/9999\n",
      "----------\n",
      "train Loss: 0.4935 Acc: 0.7787\n",
      "has spend time 36m 6s/n\n",
      "val Loss: 0.5479 Acc: 0.6928\n",
      "has spend time 36m 6s/n\n",
      "\n",
      "Epoch 1014/9999\n",
      "----------\n",
      "train Loss: 0.5338 Acc: 0.7049\n",
      "has spend time 36m 8s/n\n",
      "val Loss: 0.5478 Acc: 0.7255\n",
      "has spend time 36m 8s/n\n",
      "\n",
      "Epoch 1015/9999\n",
      "----------\n",
      "train Loss: 0.5003 Acc: 0.7459\n",
      "has spend time 36m 10s/n\n",
      "val Loss: 0.5529 Acc: 0.6993\n",
      "has spend time 36m 11s/n\n",
      "\n",
      "Epoch 1016/9999\n",
      "----------\n",
      "train Loss: 0.5181 Acc: 0.6967\n",
      "has spend time 36m 12s/n\n",
      "val Loss: 0.5580 Acc: 0.6993\n",
      "has spend time 36m 13s/n\n",
      "\n",
      "Epoch 1017/9999\n",
      "----------\n",
      "train Loss: 0.5025 Acc: 0.7459\n",
      "has spend time 36m 15s/n\n",
      "val Loss: 0.5491 Acc: 0.7059\n",
      "has spend time 36m 15s/n\n",
      "\n",
      "Epoch 1018/9999\n",
      "----------\n",
      "train Loss: 0.5119 Acc: 0.7459\n",
      "has spend time 36m 17s/n\n",
      "val Loss: 0.5546 Acc: 0.6993\n",
      "has spend time 36m 17s/n\n",
      "\n",
      "Epoch 1019/9999\n",
      "----------\n",
      "train Loss: 0.5151 Acc: 0.7418\n",
      "has spend time 36m 19s/n\n",
      "val Loss: 0.5543 Acc: 0.6928\n",
      "has spend time 36m 19s/n\n",
      "\n",
      "Epoch 1020/9999\n",
      "----------\n",
      "train Loss: 0.4942 Acc: 0.7623\n",
      "has spend time 36m 21s/n\n",
      "val Loss: 0.5441 Acc: 0.7190\n",
      "has spend time 36m 21s/n\n",
      "\n",
      "Epoch 1021/9999\n",
      "----------\n",
      "train Loss: 0.5058 Acc: 0.7377\n",
      "has spend time 36m 23s/n\n",
      "val Loss: 0.5443 Acc: 0.6993\n",
      "has spend time 36m 23s/n\n",
      "\n",
      "Epoch 1022/9999\n",
      "----------\n",
      "train Loss: 0.5001 Acc: 0.7582\n",
      "has spend time 36m 25s/n\n",
      "val Loss: 0.5533 Acc: 0.7059\n",
      "has spend time 36m 26s/n\n",
      "\n",
      "Epoch 1023/9999\n",
      "----------\n",
      "train Loss: 0.5222 Acc: 0.7377\n",
      "has spend time 36m 27s/n\n",
      "val Loss: 0.5598 Acc: 0.7059\n",
      "has spend time 36m 28s/n\n",
      "\n",
      "Epoch 1024/9999\n",
      "----------\n",
      "train Loss: 0.5339 Acc: 0.7090\n",
      "has spend time 36m 30s/n\n",
      "val Loss: 0.5522 Acc: 0.7255\n",
      "has spend time 36m 30s/n\n",
      "\n",
      "Epoch 1025/9999\n",
      "----------\n",
      "train Loss: 0.4967 Acc: 0.7418\n",
      "has spend time 36m 32s/n\n",
      "val Loss: 0.5463 Acc: 0.7190\n",
      "has spend time 36m 32s/n\n",
      "\n",
      "Epoch 1026/9999\n",
      "----------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train Loss: 0.5132 Acc: 0.7541\n",
      "has spend time 36m 34s/n\n",
      "val Loss: 0.5453 Acc: 0.7190\n",
      "has spend time 36m 34s/n\n",
      "\n",
      "Epoch 1027/9999\n",
      "----------\n",
      "train Loss: 0.5007 Acc: 0.7787\n",
      "has spend time 36m 36s/n\n",
      "val Loss: 0.5555 Acc: 0.6993\n",
      "has spend time 36m 36s/n\n",
      "\n",
      "Epoch 1028/9999\n",
      "----------\n",
      "train Loss: 0.5102 Acc: 0.7213\n",
      "has spend time 36m 38s/n\n",
      "val Loss: 0.5519 Acc: 0.6993\n",
      "has spend time 36m 38s/n\n",
      "\n",
      "Epoch 1029/9999\n",
      "----------\n",
      "train Loss: 0.5002 Acc: 0.7541\n",
      "has spend time 36m 40s/n\n",
      "val Loss: 0.5433 Acc: 0.7059\n",
      "has spend time 36m 40s/n\n",
      "\n",
      "Epoch 1030/9999\n",
      "----------\n",
      "train Loss: 0.5027 Acc: 0.7582\n",
      "has spend time 36m 42s/n\n",
      "val Loss: 0.5562 Acc: 0.7059\n",
      "has spend time 36m 42s/n\n",
      "\n",
      "Epoch 1031/9999\n",
      "----------\n",
      "train Loss: 0.5151 Acc: 0.7500\n",
      "has spend time 36m 44s/n\n",
      "val Loss: 0.5567 Acc: 0.7059\n",
      "has spend time 36m 44s/n\n",
      "\n",
      "Epoch 1032/9999\n",
      "----------\n",
      "train Loss: 0.5181 Acc: 0.7131\n",
      "has spend time 36m 46s/n\n",
      "val Loss: 0.5482 Acc: 0.7124\n",
      "has spend time 36m 47s/n\n",
      "\n",
      "Epoch 1033/9999\n",
      "----------\n",
      "train Loss: 0.5198 Acc: 0.7254\n",
      "has spend time 36m 48s/n\n",
      "val Loss: 0.5527 Acc: 0.6993\n",
      "has spend time 36m 49s/n\n",
      "\n",
      "Epoch 1034/9999\n",
      "----------\n",
      "train Loss: 0.4780 Acc: 0.7746\n",
      "has spend time 36m 51s/n\n",
      "val Loss: 0.5504 Acc: 0.6928\n",
      "has spend time 36m 51s/n\n",
      "\n",
      "Epoch 1035/9999\n",
      "----------\n",
      "train Loss: 0.5381 Acc: 0.7295\n",
      "has spend time 36m 53s/n\n",
      "val Loss: 0.5468 Acc: 0.6928\n",
      "has spend time 36m 53s/n\n",
      "\n",
      "Epoch 1036/9999\n",
      "----------\n",
      "train Loss: 0.5014 Acc: 0.7459\n",
      "has spend time 36m 55s/n\n",
      "val Loss: 0.5585 Acc: 0.6928\n",
      "has spend time 36m 55s/n\n",
      "\n",
      "Epoch 1037/9999\n",
      "----------\n",
      "train Loss: 0.4989 Acc: 0.7623\n",
      "has spend time 36m 57s/n\n",
      "val Loss: 0.5599 Acc: 0.6797\n",
      "has spend time 36m 57s/n\n",
      "\n",
      "Epoch 1038/9999\n",
      "----------\n",
      "train Loss: 0.5035 Acc: 0.7459\n",
      "has spend time 36m 59s/n\n",
      "val Loss: 0.5519 Acc: 0.7124\n",
      "has spend time 36m 59s/n\n",
      "\n",
      "Epoch 1039/9999\n",
      "----------\n",
      "train Loss: 0.5188 Acc: 0.7008\n",
      "has spend time 37m 1s/n\n",
      "val Loss: 0.5506 Acc: 0.7124\n",
      "has spend time 37m 2s/n\n",
      "\n",
      "Epoch 1040/9999\n",
      "----------\n",
      "train Loss: 0.5191 Acc: 0.7295\n",
      "has spend time 37m 3s/n\n",
      "val Loss: 0.5673 Acc: 0.6928\n",
      "has spend time 37m 4s/n\n",
      "\n",
      "Epoch 1041/9999\n",
      "----------\n",
      "train Loss: 0.5406 Acc: 0.7213\n",
      "has spend time 37m 6s/n\n",
      "val Loss: 0.5431 Acc: 0.7124\n",
      "has spend time 37m 6s/n\n",
      "\n",
      "Epoch 1042/9999\n",
      "----------\n",
      "train Loss: 0.5029 Acc: 0.7746\n",
      "has spend time 37m 8s/n\n",
      "val Loss: 0.5369 Acc: 0.7255\n",
      "has spend time 37m 8s/n\n",
      "\n",
      "Epoch 1043/9999\n",
      "----------\n",
      "train Loss: 0.4996 Acc: 0.7828\n",
      "has spend time 37m 10s/n\n",
      "val Loss: 0.5672 Acc: 0.6928\n",
      "has spend time 37m 10s/n\n",
      "\n",
      "Epoch 1044/9999\n",
      "----------\n",
      "train Loss: 0.4861 Acc: 0.7377\n",
      "has spend time 37m 12s/n\n",
      "val Loss: 0.5470 Acc: 0.7255\n",
      "has spend time 37m 12s/n\n",
      "\n",
      "Epoch 1045/9999\n",
      "----------\n",
      "train Loss: 0.4667 Acc: 0.7500\n",
      "has spend time 37m 14s/n\n",
      "val Loss: 0.5552 Acc: 0.7059\n",
      "has spend time 37m 14s/n\n",
      "\n",
      "Epoch 1046/9999\n",
      "----------\n",
      "train Loss: 0.5230 Acc: 0.7090\n",
      "has spend time 37m 16s/n\n",
      "val Loss: 0.5480 Acc: 0.7059\n",
      "has spend time 37m 17s/n\n",
      "\n",
      "Epoch 1047/9999\n",
      "----------\n",
      "train Loss: 0.5193 Acc: 0.7418\n",
      "has spend time 37m 18s/n\n",
      "val Loss: 0.5462 Acc: 0.7124\n",
      "has spend time 37m 19s/n\n",
      "\n",
      "Epoch 1048/9999\n",
      "----------\n",
      "train Loss: 0.5160 Acc: 0.7500\n",
      "has spend time 37m 20s/n\n",
      "val Loss: 0.5542 Acc: 0.7190\n",
      "has spend time 37m 21s/n\n",
      "\n",
      "Epoch 1049/9999\n",
      "----------\n",
      "train Loss: 0.4964 Acc: 0.7623\n",
      "has spend time 37m 22s/n\n",
      "val Loss: 0.5643 Acc: 0.7059\n",
      "has spend time 37m 23s/n\n",
      "\n",
      "Epoch 1050/9999\n",
      "----------\n",
      "train Loss: 0.5128 Acc: 0.6926\n",
      "has spend time 37m 24s/n\n",
      "val Loss: 0.5446 Acc: 0.6993\n",
      "has spend time 37m 25s/n\n",
      "\n",
      "Epoch 1051/9999\n",
      "----------\n",
      "train Loss: 0.4922 Acc: 0.7459\n",
      "has spend time 37m 26s/n\n",
      "val Loss: 0.5625 Acc: 0.6928\n",
      "has spend time 37m 27s/n\n",
      "\n",
      "Epoch 1052/9999\n",
      "----------\n",
      "train Loss: 0.5068 Acc: 0.7418\n",
      "has spend time 37m 29s/n\n",
      "val Loss: 0.5635 Acc: 0.7059\n",
      "has spend time 37m 30s/n\n",
      "\n",
      "Epoch 1053/9999\n",
      "----------\n",
      "train Loss: 0.5369 Acc: 0.7172\n",
      "has spend time 37m 31s/n\n",
      "val Loss: 0.5501 Acc: 0.7124\n",
      "has spend time 37m 32s/n\n",
      "\n",
      "Epoch 1054/9999\n",
      "----------\n",
      "train Loss: 0.4827 Acc: 0.7705\n",
      "has spend time 37m 33s/n\n",
      "val Loss: 0.5407 Acc: 0.7124\n",
      "has spend time 37m 34s/n\n",
      "\n",
      "Epoch 1055/9999\n",
      "----------\n",
      "train Loss: 0.4954 Acc: 0.7418\n",
      "has spend time 37m 35s/n\n",
      "val Loss: 0.5494 Acc: 0.7059\n",
      "has spend time 37m 36s/n\n",
      "\n",
      "Epoch 1056/9999\n",
      "----------\n",
      "train Loss: 0.5141 Acc: 0.7090\n",
      "has spend time 37m 37s/n\n",
      "val Loss: 0.5488 Acc: 0.7059\n",
      "has spend time 37m 38s/n\n",
      "\n",
      "Epoch 1057/9999\n",
      "----------\n",
      "train Loss: 0.5095 Acc: 0.7254\n",
      "has spend time 37m 39s/n\n",
      "val Loss: 0.5548 Acc: 0.6863\n",
      "has spend time 37m 40s/n\n",
      "\n",
      "Epoch 1058/9999\n",
      "----------\n",
      "train Loss: 0.5293 Acc: 0.6967\n",
      "has spend time 37m 42s/n\n",
      "val Loss: 0.5489 Acc: 0.6993\n",
      "has spend time 37m 42s/n\n",
      "\n",
      "Epoch 1059/9999\n",
      "----------\n",
      "train Loss: 0.5403 Acc: 0.7213\n",
      "has spend time 37m 44s/n\n",
      "val Loss: 0.5429 Acc: 0.7190\n",
      "has spend time 37m 44s/n\n",
      "\n",
      "Epoch 1060/9999\n",
      "----------\n",
      "train Loss: 0.5027 Acc: 0.7500\n",
      "has spend time 37m 46s/n\n",
      "val Loss: 0.5513 Acc: 0.7059\n",
      "has spend time 37m 46s/n\n",
      "\n",
      "Epoch 1061/9999\n",
      "----------\n",
      "train Loss: 0.5209 Acc: 0.7254\n",
      "has spend time 37m 48s/n\n",
      "val Loss: 0.5481 Acc: 0.7255\n",
      "has spend time 37m 48s/n\n",
      "\n",
      "Epoch 1062/9999\n",
      "----------\n",
      "train Loss: 0.4898 Acc: 0.7459\n",
      "has spend time 37m 50s/n\n",
      "val Loss: 0.5522 Acc: 0.7059\n",
      "has spend time 37m 50s/n\n",
      "\n",
      "Epoch 1063/9999\n",
      "----------\n",
      "train Loss: 0.4924 Acc: 0.7951\n",
      "has spend time 37m 52s/n\n",
      "val Loss: 0.5400 Acc: 0.7190\n",
      "has spend time 37m 53s/n\n",
      "\n",
      "Epoch 1064/9999\n",
      "----------\n",
      "train Loss: 0.5074 Acc: 0.7295\n",
      "has spend time 37m 54s/n\n",
      "val Loss: 0.5415 Acc: 0.7255\n",
      "has spend time 37m 55s/n\n",
      "\n",
      "Epoch 1065/9999\n",
      "----------\n",
      "train Loss: 0.5202 Acc: 0.7090\n",
      "has spend time 37m 56s/n\n",
      "val Loss: 0.5403 Acc: 0.7190\n",
      "has spend time 37m 57s/n\n",
      "\n",
      "Epoch 1066/9999\n",
      "----------\n",
      "train Loss: 0.5032 Acc: 0.7295\n",
      "has spend time 37m 58s/n\n",
      "val Loss: 0.5434 Acc: 0.7124\n",
      "has spend time 37m 59s/n\n",
      "\n",
      "Epoch 1067/9999\n",
      "----------\n",
      "train Loss: 0.5028 Acc: 0.7459\n",
      "has spend time 38m 0s/n\n",
      "val Loss: 0.5351 Acc: 0.7190\n",
      "has spend time 38m 1s/n\n",
      "\n",
      "Epoch 1068/9999\n",
      "----------\n",
      "train Loss: 0.5188 Acc: 0.7418\n",
      "has spend time 38m 2s/n\n",
      "val Loss: 0.5446 Acc: 0.7059\n",
      "has spend time 38m 3s/n\n",
      "\n",
      "Epoch 1069/9999\n",
      "----------\n",
      "train Loss: 0.4655 Acc: 0.7828\n",
      "has spend time 38m 4s/n\n",
      "val Loss: 0.5594 Acc: 0.6993\n",
      "has spend time 38m 5s/n\n",
      "\n",
      "Epoch 1070/9999\n",
      "----------\n",
      "train Loss: 0.5146 Acc: 0.7049\n",
      "has spend time 38m 7s/n\n",
      "val Loss: 0.5500 Acc: 0.7059\n",
      "has spend time 38m 7s/n\n",
      "\n",
      "Epoch 1071/9999\n",
      "----------\n",
      "train Loss: 0.4997 Acc: 0.7500\n",
      "has spend time 38m 9s/n\n",
      "val Loss: 0.5455 Acc: 0.7124\n",
      "has spend time 38m 10s/n\n",
      "\n",
      "Epoch 1072/9999\n",
      "----------\n",
      "train Loss: 0.5170 Acc: 0.7213\n",
      "has spend time 38m 11s/n\n",
      "val Loss: 0.5514 Acc: 0.7059\n",
      "has spend time 38m 12s/n\n",
      "\n",
      "Epoch 1073/9999\n",
      "----------\n",
      "train Loss: 0.4728 Acc: 0.7418\n",
      "has spend time 38m 14s/n\n",
      "val Loss: 0.5587 Acc: 0.7124\n",
      "has spend time 38m 14s/n\n",
      "\n",
      "Epoch 1074/9999\n",
      "----------\n",
      "train Loss: 0.5036 Acc: 0.7377\n",
      "has spend time 38m 16s/n\n",
      "val Loss: 0.5684 Acc: 0.6928\n",
      "has spend time 38m 16s/n\n",
      "\n",
      "Epoch 1075/9999\n",
      "----------\n",
      "train Loss: 0.5181 Acc: 0.7336\n",
      "has spend time 38m 18s/n\n",
      "val Loss: 0.5512 Acc: 0.7059\n",
      "has spend time 38m 19s/n\n",
      "\n",
      "Epoch 1076/9999\n",
      "----------\n",
      "train Loss: 0.5194 Acc: 0.7254\n",
      "has spend time 38m 20s/n\n",
      "val Loss: 0.5617 Acc: 0.7059\n",
      "has spend time 38m 21s/n\n",
      "\n",
      "Epoch 1077/9999\n",
      "----------\n",
      "train Loss: 0.4945 Acc: 0.7705\n",
      "has spend time 38m 22s/n\n",
      "val Loss: 0.5487 Acc: 0.7124\n",
      "has spend time 38m 23s/n\n",
      "\n",
      "Epoch 1078/9999\n",
      "----------\n",
      "train Loss: 0.4744 Acc: 0.7582\n",
      "has spend time 38m 24s/n\n",
      "val Loss: 0.5499 Acc: 0.7059\n",
      "has spend time 38m 25s/n\n",
      "\n",
      "Epoch 1079/9999\n",
      "----------\n",
      "train Loss: 0.5367 Acc: 0.6598\n",
      "has spend time 38m 26s/n\n",
      "val Loss: 0.5423 Acc: 0.7124\n",
      "has spend time 38m 27s/n\n",
      "\n",
      "Epoch 1080/9999\n",
      "----------\n",
      "train Loss: 0.5097 Acc: 0.7377\n",
      "has spend time 38m 28s/n\n",
      "val Loss: 0.5590 Acc: 0.6928\n",
      "has spend time 38m 29s/n\n",
      "\n",
      "Epoch 1081/9999\n",
      "----------\n",
      "train Loss: 0.5304 Acc: 0.7336\n",
      "has spend time 38m 30s/n\n",
      "val Loss: 0.5452 Acc: 0.7059\n",
      "has spend time 38m 31s/n\n",
      "\n",
      "Epoch 1082/9999\n",
      "----------\n",
      "train Loss: 0.5523 Acc: 0.7295\n",
      "has spend time 38m 33s/n\n",
      "val Loss: 0.5426 Acc: 0.6993\n",
      "has spend time 38m 33s/n\n",
      "\n",
      "Epoch 1083/9999\n",
      "----------\n",
      "train Loss: 0.5207 Acc: 0.7008\n",
      "has spend time 38m 35s/n\n",
      "val Loss: 0.5458 Acc: 0.7124\n",
      "has spend time 38m 35s/n\n",
      "\n",
      "Epoch 1084/9999\n",
      "----------\n",
      "train Loss: 0.4991 Acc: 0.7336\n",
      "has spend time 38m 37s/n\n",
      "val Loss: 0.5524 Acc: 0.7124\n",
      "has spend time 38m 37s/n\n",
      "\n",
      "Epoch 1085/9999\n",
      "----------\n",
      "train Loss: 0.5086 Acc: 0.7254\n",
      "has spend time 38m 39s/n\n",
      "val Loss: 0.5473 Acc: 0.6928\n",
      "has spend time 38m 40s/n\n",
      "\n",
      "Epoch 1086/9999\n",
      "----------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train Loss: 0.5243 Acc: 0.7295\n",
      "has spend time 38m 41s/n\n",
      "val Loss: 0.5445 Acc: 0.7124\n",
      "has spend time 38m 42s/n\n",
      "\n",
      "Epoch 1087/9999\n",
      "----------\n",
      "train Loss: 0.4915 Acc: 0.7500\n",
      "has spend time 38m 43s/n\n",
      "val Loss: 0.5517 Acc: 0.7190\n",
      "has spend time 38m 44s/n\n",
      "\n",
      "Epoch 1088/9999\n",
      "----------\n",
      "train Loss: 0.4647 Acc: 0.7910\n",
      "has spend time 38m 46s/n\n",
      "val Loss: 0.5424 Acc: 0.7059\n",
      "has spend time 38m 47s/n\n",
      "\n",
      "Epoch 1089/9999\n",
      "----------\n",
      "train Loss: 0.5138 Acc: 0.7664\n",
      "has spend time 38m 48s/n\n",
      "val Loss: 0.5483 Acc: 0.7190\n",
      "has spend time 38m 49s/n\n",
      "\n",
      "Epoch 1090/9999\n",
      "----------\n",
      "train Loss: 0.5413 Acc: 0.7090\n",
      "has spend time 38m 50s/n\n",
      "val Loss: 0.5488 Acc: 0.7124\n",
      "has spend time 38m 51s/n\n",
      "\n",
      "Epoch 1091/9999\n",
      "----------\n",
      "train Loss: 0.5145 Acc: 0.7336\n",
      "has spend time 38m 52s/n\n",
      "val Loss: 0.5516 Acc: 0.7059\n",
      "has spend time 38m 53s/n\n",
      "\n",
      "Epoch 1092/9999\n",
      "----------\n",
      "train Loss: 0.4782 Acc: 0.7500\n",
      "has spend time 38m 54s/n\n",
      "val Loss: 0.5488 Acc: 0.7124\n",
      "has spend time 38m 55s/n\n",
      "\n",
      "Epoch 1093/9999\n",
      "----------\n",
      "train Loss: 0.5084 Acc: 0.7254\n",
      "has spend time 38m 57s/n\n",
      "val Loss: 0.5485 Acc: 0.7190\n",
      "has spend time 38m 58s/n\n",
      "\n",
      "Epoch 1094/9999\n",
      "----------\n",
      "train Loss: 0.4835 Acc: 0.7664\n",
      "has spend time 38m 59s/n\n",
      "val Loss: 0.5519 Acc: 0.7124\n",
      "has spend time 38m 60s/n\n",
      "\n",
      "Epoch 1095/9999\n",
      "----------\n",
      "train Loss: 0.4748 Acc: 0.7787\n",
      "has spend time 39m 1s/n\n",
      "val Loss: 0.5471 Acc: 0.7124\n",
      "has spend time 39m 2s/n\n",
      "\n",
      "Epoch 1096/9999\n",
      "----------\n",
      "train Loss: 0.5051 Acc: 0.7582\n",
      "has spend time 39m 3s/n\n",
      "val Loss: 0.5491 Acc: 0.7190\n",
      "has spend time 39m 4s/n\n",
      "\n",
      "Epoch 1097/9999\n",
      "----------\n",
      "train Loss: 0.4760 Acc: 0.7623\n",
      "has spend time 39m 6s/n\n",
      "val Loss: 0.5578 Acc: 0.6993\n",
      "has spend time 39m 6s/n\n",
      "\n",
      "Epoch 1098/9999\n",
      "----------\n",
      "train Loss: 0.4949 Acc: 0.7541\n",
      "has spend time 39m 8s/n\n",
      "val Loss: 0.5492 Acc: 0.7059\n",
      "has spend time 39m 8s/n\n",
      "\n",
      "Epoch 1099/9999\n",
      "----------\n",
      "train Loss: 0.5058 Acc: 0.7541\n",
      "has spend time 39m 10s/n\n",
      "val Loss: 0.5628 Acc: 0.6797\n",
      "has spend time 39m 10s/n\n",
      "\n",
      "Epoch 1100/9999\n",
      "----------\n",
      "train Loss: 0.5109 Acc: 0.7336\n",
      "has spend time 39m 12s/n\n",
      "val Loss: 0.5468 Acc: 0.7059\n",
      "has spend time 39m 12s/n\n",
      "\n",
      "Epoch 1101/9999\n",
      "----------\n",
      "train Loss: 0.4884 Acc: 0.7582\n",
      "has spend time 39m 14s/n\n",
      "val Loss: 0.5547 Acc: 0.7059\n",
      "has spend time 39m 15s/n\n",
      "\n",
      "Epoch 1102/9999\n",
      "----------\n",
      "train Loss: 0.4903 Acc: 0.7500\n",
      "has spend time 39m 16s/n\n",
      "val Loss: 0.5405 Acc: 0.7190\n",
      "has spend time 39m 17s/n\n",
      "\n",
      "Epoch 1103/9999\n",
      "----------\n",
      "train Loss: 0.4929 Acc: 0.7705\n",
      "has spend time 39m 19s/n\n",
      "val Loss: 0.5587 Acc: 0.7124\n",
      "has spend time 39m 19s/n\n",
      "\n",
      "Epoch 1104/9999\n",
      "----------\n",
      "train Loss: 0.5233 Acc: 0.7131\n",
      "has spend time 39m 21s/n\n",
      "val Loss: 0.5521 Acc: 0.7059\n",
      "has spend time 39m 21s/n\n",
      "\n",
      "Epoch 1105/9999\n",
      "----------\n",
      "train Loss: 0.4628 Acc: 0.7869\n",
      "has spend time 39m 23s/n\n",
      "val Loss: 0.5564 Acc: 0.6993\n",
      "has spend time 39m 24s/n\n",
      "\n",
      "Epoch 1106/9999\n",
      "----------\n",
      "train Loss: 0.4960 Acc: 0.7090\n",
      "has spend time 39m 25s/n\n",
      "val Loss: 0.5551 Acc: 0.6993\n",
      "has spend time 39m 26s/n\n",
      "\n",
      "Epoch 1107/9999\n",
      "----------\n",
      "train Loss: 0.5098 Acc: 0.7418\n",
      "has spend time 39m 27s/n\n",
      "val Loss: 0.5404 Acc: 0.7124\n",
      "has spend time 39m 28s/n\n",
      "\n",
      "Epoch 1108/9999\n",
      "----------\n",
      "train Loss: 0.5263 Acc: 0.7049\n",
      "has spend time 39m 30s/n\n",
      "val Loss: 0.5549 Acc: 0.6863\n",
      "has spend time 39m 30s/n\n",
      "\n",
      "Epoch 1109/9999\n",
      "----------\n",
      "train Loss: 0.5312 Acc: 0.7131\n",
      "has spend time 39m 32s/n\n",
      "val Loss: 0.5564 Acc: 0.7255\n",
      "has spend time 39m 32s/n\n",
      "\n",
      "Epoch 1110/9999\n",
      "----------\n",
      "train Loss: 0.5256 Acc: 0.7336\n",
      "has spend time 39m 34s/n\n",
      "val Loss: 0.5484 Acc: 0.6993\n",
      "has spend time 39m 34s/n\n",
      "\n",
      "Epoch 1111/9999\n",
      "----------\n",
      "train Loss: 0.5148 Acc: 0.7418\n",
      "has spend time 39m 36s/n\n",
      "val Loss: 0.5513 Acc: 0.7059\n",
      "has spend time 39m 37s/n\n",
      "\n",
      "Epoch 1112/9999\n",
      "----------\n",
      "train Loss: 0.5229 Acc: 0.7664\n",
      "has spend time 39m 38s/n\n",
      "val Loss: 0.5493 Acc: 0.7190\n",
      "has spend time 39m 39s/n\n",
      "\n",
      "Epoch 1113/9999\n",
      "----------\n",
      "train Loss: 0.4944 Acc: 0.7541\n",
      "has spend time 39m 41s/n\n",
      "val Loss: 0.5504 Acc: 0.7124\n",
      "has spend time 39m 41s/n\n",
      "\n",
      "Epoch 1114/9999\n",
      "----------\n",
      "train Loss: 0.5410 Acc: 0.7213\n",
      "has spend time 39m 43s/n\n",
      "val Loss: 0.5557 Acc: 0.7059\n",
      "has spend time 39m 43s/n\n",
      "\n",
      "Epoch 1115/9999\n",
      "----------\n",
      "train Loss: 0.5303 Acc: 0.7213\n",
      "has spend time 39m 45s/n\n",
      "val Loss: 0.5457 Acc: 0.7124\n",
      "has spend time 39m 45s/n\n",
      "\n",
      "Epoch 1116/9999\n",
      "----------\n",
      "train Loss: 0.4771 Acc: 0.7705\n",
      "has spend time 39m 47s/n\n",
      "val Loss: 0.5410 Acc: 0.7059\n",
      "has spend time 39m 47s/n\n",
      "\n",
      "Epoch 1117/9999\n",
      "----------\n",
      "train Loss: 0.5116 Acc: 0.7336\n",
      "has spend time 39m 49s/n\n",
      "val Loss: 0.5583 Acc: 0.7124\n",
      "has spend time 39m 50s/n\n",
      "\n",
      "Epoch 1118/9999\n",
      "----------\n",
      "train Loss: 0.5040 Acc: 0.7213\n",
      "has spend time 39m 51s/n\n",
      "val Loss: 0.5528 Acc: 0.7059\n",
      "has spend time 39m 52s/n\n",
      "\n",
      "Epoch 1119/9999\n",
      "----------\n",
      "train Loss: 0.5319 Acc: 0.7213\n",
      "has spend time 39m 54s/n\n",
      "val Loss: 0.5555 Acc: 0.6993\n",
      "has spend time 39m 54s/n\n",
      "\n",
      "Epoch 1120/9999\n",
      "----------\n",
      "train Loss: 0.5208 Acc: 0.7090\n",
      "has spend time 39m 56s/n\n",
      "val Loss: 0.5520 Acc: 0.7124\n",
      "has spend time 39m 56s/n\n",
      "\n",
      "Epoch 1121/9999\n",
      "----------\n",
      "train Loss: 0.4733 Acc: 0.7418\n",
      "has spend time 39m 58s/n\n",
      "val Loss: 0.5635 Acc: 0.6993\n",
      "has spend time 39m 58s/n\n",
      "\n",
      "Epoch 1122/9999\n",
      "----------\n",
      "train Loss: 0.5192 Acc: 0.7295\n",
      "has spend time 39m 60s/n\n",
      "val Loss: 0.5551 Acc: 0.7059\n",
      "has spend time 40m 0s/n\n",
      "\n",
      "Epoch 1123/9999\n",
      "----------\n",
      "train Loss: 0.4957 Acc: 0.7541\n",
      "has spend time 40m 2s/n\n",
      "val Loss: 0.5509 Acc: 0.7190\n",
      "has spend time 40m 2s/n\n",
      "\n",
      "Epoch 1124/9999\n",
      "----------\n",
      "train Loss: 0.5333 Acc: 0.7295\n",
      "has spend time 40m 4s/n\n",
      "val Loss: 0.5505 Acc: 0.7059\n",
      "has spend time 40m 5s/n\n",
      "\n",
      "Epoch 1125/9999\n",
      "----------\n",
      "train Loss: 0.4951 Acc: 0.7336\n",
      "has spend time 40m 6s/n\n",
      "val Loss: 0.5582 Acc: 0.6993\n",
      "has spend time 40m 7s/n\n",
      "\n",
      "Epoch 1126/9999\n",
      "----------\n",
      "train Loss: 0.4876 Acc: 0.7336\n",
      "has spend time 40m 9s/n\n",
      "val Loss: 0.5515 Acc: 0.6993\n",
      "has spend time 40m 9s/n\n",
      "\n",
      "Epoch 1127/9999\n",
      "----------\n",
      "train Loss: 0.5270 Acc: 0.7500\n",
      "has spend time 40m 11s/n\n",
      "val Loss: 0.5504 Acc: 0.7190\n",
      "has spend time 40m 11s/n\n",
      "\n",
      "Epoch 1128/9999\n",
      "----------\n",
      "train Loss: 0.5358 Acc: 0.7213\n",
      "has spend time 40m 13s/n\n",
      "val Loss: 0.5434 Acc: 0.7190\n",
      "has spend time 40m 14s/n\n",
      "\n",
      "Epoch 1129/9999\n",
      "----------\n",
      "train Loss: 0.5093 Acc: 0.7295\n",
      "has spend time 40m 15s/n\n",
      "val Loss: 0.5461 Acc: 0.7124\n",
      "has spend time 40m 16s/n\n",
      "\n",
      "Epoch 1130/9999\n",
      "----------\n",
      "train Loss: 0.5134 Acc: 0.6844\n",
      "has spend time 40m 17s/n\n",
      "val Loss: 0.5535 Acc: 0.6928\n",
      "has spend time 40m 18s/n\n",
      "\n",
      "Epoch 1131/9999\n",
      "----------\n",
      "train Loss: 0.4913 Acc: 0.7623\n",
      "has spend time 40m 20s/n\n",
      "val Loss: 0.5512 Acc: 0.7124\n",
      "has spend time 40m 20s/n\n",
      "\n",
      "Epoch 1132/9999\n",
      "----------\n",
      "train Loss: 0.5372 Acc: 0.7336\n",
      "has spend time 40m 22s/n\n",
      "val Loss: 0.5733 Acc: 0.6928\n",
      "has spend time 40m 23s/n\n",
      "\n",
      "Epoch 1133/9999\n",
      "----------\n",
      "train Loss: 0.5166 Acc: 0.7172\n",
      "has spend time 40m 24s/n\n",
      "val Loss: 0.5682 Acc: 0.6863\n",
      "has spend time 40m 25s/n\n",
      "\n",
      "Epoch 1134/9999\n",
      "----------\n",
      "train Loss: 0.4885 Acc: 0.7418\n",
      "has spend time 40m 26s/n\n",
      "val Loss: 0.5544 Acc: 0.6928\n",
      "has spend time 40m 27s/n\n",
      "\n",
      "Epoch 1135/9999\n",
      "----------\n",
      "train Loss: 0.5022 Acc: 0.7459\n",
      "has spend time 40m 28s/n\n",
      "val Loss: 0.5440 Acc: 0.7255\n",
      "has spend time 40m 29s/n\n",
      "\n",
      "Epoch 1136/9999\n",
      "----------\n",
      "train Loss: 0.5174 Acc: 0.7131\n",
      "has spend time 40m 30s/n\n",
      "val Loss: 0.5513 Acc: 0.7255\n",
      "has spend time 40m 31s/n\n",
      "\n",
      "Epoch 1137/9999\n",
      "----------\n",
      "train Loss: 0.5460 Acc: 0.7418\n",
      "has spend time 40m 33s/n\n",
      "val Loss: 0.5506 Acc: 0.7190\n",
      "has spend time 40m 33s/n\n",
      "\n",
      "Epoch 1138/9999\n",
      "----------\n",
      "train Loss: 0.5028 Acc: 0.7541\n",
      "has spend time 40m 35s/n\n",
      "val Loss: 0.5441 Acc: 0.7190\n",
      "has spend time 40m 36s/n\n",
      "\n",
      "Epoch 1139/9999\n",
      "----------\n",
      "train Loss: 0.5055 Acc: 0.7541\n",
      "has spend time 40m 37s/n\n",
      "val Loss: 0.5542 Acc: 0.7124\n",
      "has spend time 40m 38s/n\n",
      "\n",
      "Epoch 1140/9999\n",
      "----------\n",
      "train Loss: 0.5011 Acc: 0.7705\n",
      "has spend time 40m 39s/n\n",
      "val Loss: 0.5582 Acc: 0.7059\n",
      "has spend time 40m 40s/n\n",
      "\n",
      "Epoch 1141/9999\n",
      "----------\n",
      "train Loss: 0.5296 Acc: 0.7090\n",
      "has spend time 40m 42s/n\n",
      "val Loss: 0.5550 Acc: 0.6993\n",
      "has spend time 40m 42s/n\n",
      "\n",
      "Epoch 1142/9999\n",
      "----------\n",
      "train Loss: 0.5009 Acc: 0.7377\n",
      "has spend time 40m 44s/n\n",
      "val Loss: 0.5589 Acc: 0.6993\n",
      "has spend time 40m 44s/n\n",
      "\n",
      "Epoch 1143/9999\n",
      "----------\n",
      "train Loss: 0.5116 Acc: 0.7500\n",
      "has spend time 40m 46s/n\n",
      "val Loss: 0.5619 Acc: 0.6928\n",
      "has spend time 40m 47s/n\n",
      "\n",
      "Epoch 1144/9999\n",
      "----------\n",
      "train Loss: 0.5250 Acc: 0.7213\n",
      "has spend time 40m 48s/n\n",
      "val Loss: 0.5436 Acc: 0.7124\n",
      "has spend time 40m 49s/n\n",
      "\n",
      "Epoch 1145/9999\n",
      "----------\n",
      "train Loss: 0.4961 Acc: 0.7254\n",
      "has spend time 40m 50s/n\n",
      "val Loss: 0.5423 Acc: 0.7190\n",
      "has spend time 40m 51s/n\n",
      "\n",
      "Epoch 1146/9999\n",
      "----------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train Loss: 0.4793 Acc: 0.7500\n",
      "has spend time 40m 52s/n\n",
      "val Loss: 0.5533 Acc: 0.6993\n",
      "has spend time 40m 53s/n\n",
      "\n",
      "Epoch 1147/9999\n",
      "----------\n",
      "train Loss: 0.4754 Acc: 0.7336\n",
      "has spend time 40m 54s/n\n",
      "val Loss: 0.5573 Acc: 0.7059\n",
      "has spend time 40m 55s/n\n",
      "\n",
      "Epoch 1148/9999\n",
      "----------\n",
      "train Loss: 0.5269 Acc: 0.7336\n",
      "has spend time 40m 56s/n\n",
      "val Loss: 0.5597 Acc: 0.6993\n",
      "has spend time 40m 57s/n\n",
      "\n",
      "Epoch 1149/9999\n",
      "----------\n",
      "train Loss: 0.5052 Acc: 0.7377\n",
      "has spend time 40m 58s/n\n",
      "val Loss: 0.5563 Acc: 0.6928\n",
      "has spend time 40m 59s/n\n",
      "\n",
      "Epoch 1150/9999\n",
      "----------\n",
      "train Loss: 0.5138 Acc: 0.7213\n",
      "has spend time 41m 1s/n\n",
      "val Loss: 0.5578 Acc: 0.6993\n",
      "has spend time 41m 1s/n\n",
      "\n",
      "Epoch 1151/9999\n",
      "----------\n",
      "train Loss: 0.5091 Acc: 0.7254\n",
      "has spend time 41m 3s/n\n",
      "val Loss: 0.5632 Acc: 0.6863\n",
      "has spend time 41m 4s/n\n",
      "\n",
      "Epoch 1152/9999\n",
      "----------\n",
      "train Loss: 0.5180 Acc: 0.7213\n",
      "has spend time 41m 5s/n\n",
      "val Loss: 0.5520 Acc: 0.6928\n",
      "has spend time 41m 6s/n\n",
      "\n",
      "Epoch 1153/9999\n",
      "----------\n",
      "train Loss: 0.5167 Acc: 0.7172\n",
      "has spend time 41m 7s/n\n",
      "val Loss: 0.5574 Acc: 0.6928\n",
      "has spend time 41m 8s/n\n",
      "\n",
      "Epoch 1154/9999\n",
      "----------\n",
      "train Loss: 0.5102 Acc: 0.7377\n",
      "has spend time 41m 10s/n\n",
      "val Loss: 0.5532 Acc: 0.7059\n",
      "has spend time 41m 10s/n\n",
      "\n",
      "Epoch 1155/9999\n",
      "----------\n",
      "train Loss: 0.5084 Acc: 0.7418\n",
      "has spend time 41m 12s/n\n",
      "val Loss: 0.5515 Acc: 0.6993\n",
      "has spend time 41m 13s/n\n",
      "\n",
      "Epoch 1156/9999\n",
      "----------\n",
      "train Loss: 0.5336 Acc: 0.6844\n",
      "has spend time 41m 14s/n\n",
      "val Loss: 0.5512 Acc: 0.7124\n",
      "has spend time 41m 15s/n\n",
      "\n",
      "Epoch 1157/9999\n",
      "----------\n",
      "train Loss: 0.5192 Acc: 0.7131\n",
      "has spend time 41m 16s/n\n",
      "val Loss: 0.5427 Acc: 0.7190\n",
      "has spend time 41m 17s/n\n",
      "\n",
      "Epoch 1158/9999\n",
      "----------\n",
      "train Loss: 0.4685 Acc: 0.7623\n",
      "has spend time 41m 18s/n\n",
      "val Loss: 0.5537 Acc: 0.7124\n",
      "has spend time 41m 19s/n\n",
      "\n",
      "Epoch 1159/9999\n",
      "----------\n",
      "train Loss: 0.5209 Acc: 0.7418\n",
      "has spend time 41m 20s/n\n",
      "val Loss: 0.5640 Acc: 0.6993\n",
      "has spend time 41m 21s/n\n",
      "\n",
      "Epoch 1160/9999\n",
      "----------\n",
      "train Loss: 0.5185 Acc: 0.7295\n",
      "has spend time 41m 22s/n\n",
      "val Loss: 0.5528 Acc: 0.7059\n",
      "has spend time 41m 23s/n\n",
      "\n",
      "Epoch 1161/9999\n",
      "----------\n",
      "train Loss: 0.5092 Acc: 0.7377\n",
      "has spend time 41m 25s/n\n",
      "val Loss: 0.5527 Acc: 0.7059\n",
      "has spend time 41m 26s/n\n",
      "\n",
      "Epoch 1162/9999\n",
      "----------\n",
      "train Loss: 0.4956 Acc: 0.7664\n",
      "has spend time 41m 27s/n\n",
      "val Loss: 0.5583 Acc: 0.7059\n",
      "has spend time 41m 28s/n\n",
      "\n",
      "Epoch 1163/9999\n",
      "----------\n",
      "train Loss: 0.4881 Acc: 0.7705\n",
      "has spend time 41m 29s/n\n",
      "val Loss: 0.5652 Acc: 0.6993\n",
      "has spend time 41m 30s/n\n",
      "\n",
      "Epoch 1164/9999\n",
      "----------\n",
      "train Loss: 0.4891 Acc: 0.7131\n",
      "has spend time 41m 31s/n\n",
      "val Loss: 0.5668 Acc: 0.6993\n",
      "has spend time 41m 32s/n\n",
      "\n",
      "Epoch 1165/9999\n",
      "----------\n",
      "train Loss: 0.4963 Acc: 0.7500\n",
      "has spend time 41m 33s/n\n",
      "val Loss: 0.5515 Acc: 0.7124\n",
      "has spend time 41m 34s/n\n",
      "\n",
      "Epoch 1166/9999\n",
      "----------\n",
      "train Loss: 0.5091 Acc: 0.7418\n",
      "has spend time 41m 35s/n\n",
      "val Loss: 0.5448 Acc: 0.7059\n",
      "has spend time 41m 36s/n\n",
      "\n",
      "Epoch 1167/9999\n",
      "----------\n",
      "train Loss: 0.5011 Acc: 0.7254\n",
      "has spend time 41m 38s/n\n",
      "val Loss: 0.5508 Acc: 0.7059\n",
      "has spend time 41m 38s/n\n",
      "\n",
      "Epoch 1168/9999\n",
      "----------\n",
      "train Loss: 0.5092 Acc: 0.7131\n",
      "has spend time 41m 40s/n\n",
      "val Loss: 0.5533 Acc: 0.6993\n",
      "has spend time 41m 40s/n\n",
      "\n",
      "Epoch 1169/9999\n",
      "----------\n",
      "train Loss: 0.5299 Acc: 0.6967\n",
      "has spend time 41m 42s/n\n",
      "val Loss: 0.5628 Acc: 0.7059\n",
      "has spend time 41m 42s/n\n",
      "\n",
      "Epoch 1170/9999\n",
      "----------\n",
      "train Loss: 0.5166 Acc: 0.7049\n",
      "has spend time 41m 44s/n\n",
      "val Loss: 0.5458 Acc: 0.7124\n",
      "has spend time 41m 45s/n\n",
      "\n",
      "Epoch 1171/9999\n",
      "----------\n",
      "train Loss: 0.5034 Acc: 0.7295\n",
      "has spend time 41m 46s/n\n",
      "val Loss: 0.5469 Acc: 0.7255\n",
      "has spend time 41m 47s/n\n",
      "\n",
      "Epoch 1172/9999\n",
      "----------\n",
      "train Loss: 0.5022 Acc: 0.7459\n",
      "has spend time 41m 48s/n\n",
      "val Loss: 0.5586 Acc: 0.6993\n",
      "has spend time 41m 49s/n\n",
      "\n",
      "Epoch 1173/9999\n",
      "----------\n",
      "train Loss: 0.4819 Acc: 0.7664\n",
      "has spend time 41m 50s/n\n",
      "val Loss: 0.5557 Acc: 0.7059\n",
      "has spend time 41m 51s/n\n",
      "\n",
      "Epoch 1174/9999\n",
      "----------\n",
      "train Loss: 0.4843 Acc: 0.7418\n",
      "has spend time 41m 52s/n\n",
      "val Loss: 0.5631 Acc: 0.6928\n",
      "has spend time 41m 53s/n\n",
      "\n",
      "Epoch 1175/9999\n",
      "----------\n",
      "train Loss: 0.5492 Acc: 0.6926\n",
      "has spend time 41m 54s/n\n",
      "val Loss: 0.5519 Acc: 0.6928\n",
      "has spend time 41m 55s/n\n",
      "\n",
      "Epoch 1176/9999\n",
      "----------\n",
      "train Loss: 0.5048 Acc: 0.7172\n",
      "has spend time 41m 56s/n\n",
      "val Loss: 0.5467 Acc: 0.7190\n",
      "has spend time 41m 57s/n\n",
      "\n",
      "Epoch 1177/9999\n",
      "----------\n",
      "train Loss: 0.5024 Acc: 0.7213\n",
      "has spend time 41m 58s/n\n",
      "val Loss: 0.5422 Acc: 0.7124\n",
      "has spend time 41m 59s/n\n",
      "\n",
      "Epoch 1178/9999\n",
      "----------\n",
      "train Loss: 0.5096 Acc: 0.7500\n",
      "has spend time 42m 1s/n\n",
      "val Loss: 0.5482 Acc: 0.6993\n",
      "has spend time 42m 1s/n\n",
      "\n",
      "Epoch 1179/9999\n",
      "----------\n",
      "train Loss: 0.5062 Acc: 0.7172\n",
      "has spend time 42m 3s/n\n",
      "val Loss: 0.5544 Acc: 0.7059\n",
      "has spend time 42m 3s/n\n",
      "\n",
      "Epoch 1180/9999\n",
      "----------\n",
      "train Loss: 0.5041 Acc: 0.7418\n",
      "has spend time 42m 5s/n\n",
      "val Loss: 0.5521 Acc: 0.7124\n",
      "has spend time 42m 5s/n\n",
      "\n",
      "Epoch 1181/9999\n",
      "----------\n",
      "train Loss: 0.5390 Acc: 0.7049\n",
      "has spend time 42m 7s/n\n",
      "val Loss: 0.5547 Acc: 0.6993\n",
      "has spend time 42m 7s/n\n",
      "\n",
      "Epoch 1182/9999\n",
      "----------\n",
      "train Loss: 0.5160 Acc: 0.7377\n",
      "has spend time 42m 9s/n\n",
      "val Loss: 0.5568 Acc: 0.7059\n",
      "has spend time 42m 9s/n\n",
      "\n",
      "Epoch 1183/9999\n",
      "----------\n",
      "train Loss: 0.4913 Acc: 0.7500\n",
      "has spend time 42m 11s/n\n",
      "val Loss: 0.5410 Acc: 0.7255\n",
      "has spend time 42m 12s/n\n",
      "\n",
      "Epoch 1184/9999\n",
      "----------\n",
      "train Loss: 0.5089 Acc: 0.7623\n",
      "has spend time 42m 13s/n\n",
      "val Loss: 0.5657 Acc: 0.6928\n",
      "has spend time 42m 14s/n\n",
      "\n",
      "Epoch 1185/9999\n",
      "----------\n",
      "train Loss: 0.5030 Acc: 0.7418\n",
      "has spend time 42m 16s/n\n",
      "val Loss: 0.5482 Acc: 0.7059\n",
      "has spend time 42m 16s/n\n",
      "\n",
      "Epoch 1186/9999\n",
      "----------\n",
      "train Loss: 0.5004 Acc: 0.7623\n",
      "has spend time 42m 18s/n\n",
      "val Loss: 0.5608 Acc: 0.6993\n",
      "has spend time 42m 18s/n\n",
      "\n",
      "Epoch 1187/9999\n",
      "----------\n",
      "train Loss: 0.4686 Acc: 0.7377\n",
      "has spend time 42m 20s/n\n",
      "val Loss: 0.5514 Acc: 0.7059\n",
      "has spend time 42m 20s/n\n",
      "\n",
      "Epoch 1188/9999\n",
      "----------\n",
      "train Loss: 0.5288 Acc: 0.7295\n",
      "has spend time 42m 22s/n\n",
      "val Loss: 0.5415 Acc: 0.7124\n",
      "has spend time 42m 22s/n\n",
      "\n",
      "Epoch 1189/9999\n",
      "----------\n",
      "train Loss: 0.5154 Acc: 0.7172\n",
      "has spend time 42m 24s/n\n",
      "val Loss: 0.5480 Acc: 0.7124\n",
      "has spend time 42m 25s/n\n",
      "\n",
      "Epoch 1190/9999\n",
      "----------\n",
      "train Loss: 0.5072 Acc: 0.7500\n",
      "has spend time 42m 26s/n\n",
      "val Loss: 0.5509 Acc: 0.7124\n",
      "has spend time 42m 27s/n\n",
      "\n",
      "Epoch 1191/9999\n",
      "----------\n",
      "train Loss: 0.5324 Acc: 0.7213\n",
      "has spend time 42m 28s/n\n",
      "val Loss: 0.5543 Acc: 0.6993\n",
      "has spend time 42m 29s/n\n",
      "\n",
      "Epoch 1192/9999\n",
      "----------\n",
      "train Loss: 0.5068 Acc: 0.7336\n",
      "has spend time 42m 31s/n\n",
      "val Loss: 0.5549 Acc: 0.6928\n",
      "has spend time 42m 31s/n\n",
      "\n",
      "Epoch 1193/9999\n",
      "----------\n",
      "train Loss: 0.5003 Acc: 0.7418\n",
      "has spend time 42m 33s/n\n",
      "val Loss: 0.5465 Acc: 0.7190\n",
      "has spend time 42m 33s/n\n",
      "\n",
      "Epoch 1194/9999\n",
      "----------\n",
      "train Loss: 0.5199 Acc: 0.7254\n",
      "has spend time 42m 35s/n\n",
      "val Loss: 0.5485 Acc: 0.7124\n",
      "has spend time 42m 35s/n\n",
      "\n",
      "Epoch 1195/9999\n",
      "----------\n",
      "train Loss: 0.4904 Acc: 0.7582\n",
      "has spend time 42m 37s/n\n",
      "val Loss: 0.5548 Acc: 0.7059\n",
      "has spend time 42m 37s/n\n",
      "\n",
      "Epoch 1196/9999\n",
      "----------\n",
      "train Loss: 0.5317 Acc: 0.7377\n",
      "has spend time 42m 39s/n\n",
      "val Loss: 0.5529 Acc: 0.7059\n",
      "has spend time 42m 39s/n\n",
      "\n",
      "Epoch 1197/9999\n",
      "----------\n",
      "train Loss: 0.5383 Acc: 0.7131\n",
      "has spend time 42m 41s/n\n",
      "val Loss: 0.5609 Acc: 0.7124\n",
      "has spend time 42m 42s/n\n",
      "\n",
      "Epoch 1198/9999\n",
      "----------\n",
      "train Loss: 0.4997 Acc: 0.7664\n",
      "has spend time 42m 43s/n\n",
      "val Loss: 0.5503 Acc: 0.7124\n",
      "has spend time 42m 44s/n\n",
      "\n",
      "Epoch 1199/9999\n",
      "----------\n",
      "train Loss: 0.4954 Acc: 0.7377\n",
      "has spend time 42m 45s/n\n",
      "val Loss: 0.5558 Acc: 0.6993\n",
      "has spend time 42m 46s/n\n",
      "\n",
      "Epoch 1200/9999\n",
      "----------\n",
      "train Loss: 0.4954 Acc: 0.7869\n",
      "has spend time 42m 47s/n\n",
      "val Loss: 0.5595 Acc: 0.6993\n",
      "has spend time 42m 48s/n\n",
      "\n",
      "Epoch 1201/9999\n",
      "----------\n",
      "train Loss: 0.5188 Acc: 0.7377\n",
      "has spend time 42m 49s/n\n",
      "val Loss: 0.5452 Acc: 0.7190\n",
      "has spend time 42m 50s/n\n",
      "\n",
      "Epoch 1202/9999\n",
      "----------\n",
      "train Loss: 0.5512 Acc: 0.6967\n",
      "has spend time 42m 51s/n\n",
      "val Loss: 0.5571 Acc: 0.6993\n",
      "has spend time 42m 52s/n\n",
      "\n",
      "Epoch 1203/9999\n",
      "----------\n",
      "train Loss: 0.5111 Acc: 0.7418\n",
      "has spend time 42m 54s/n\n",
      "val Loss: 0.5474 Acc: 0.7059\n",
      "has spend time 42m 54s/n\n",
      "\n",
      "Epoch 1204/9999\n",
      "----------\n",
      "train Loss: 0.5138 Acc: 0.7623\n",
      "has spend time 42m 56s/n\n",
      "val Loss: 0.5424 Acc: 0.7124\n",
      "has spend time 42m 56s/n\n",
      "\n",
      "Epoch 1205/9999\n",
      "----------\n",
      "train Loss: 0.5116 Acc: 0.7295\n",
      "has spend time 42m 58s/n\n",
      "val Loss: 0.5485 Acc: 0.7190\n",
      "has spend time 42m 59s/n\n",
      "\n",
      "Epoch 1206/9999\n",
      "----------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train Loss: 0.5241 Acc: 0.7336\n",
      "has spend time 43m 0s/n\n",
      "val Loss: 0.5539 Acc: 0.7059\n",
      "has spend time 43m 1s/n\n",
      "\n",
      "Epoch 1207/9999\n",
      "----------\n",
      "train Loss: 0.5067 Acc: 0.7541\n",
      "has spend time 43m 2s/n\n",
      "val Loss: 0.5498 Acc: 0.7124\n",
      "has spend time 43m 3s/n\n",
      "\n",
      "Epoch 1208/9999\n",
      "----------\n",
      "train Loss: 0.5099 Acc: 0.7295\n",
      "has spend time 43m 5s/n\n",
      "val Loss: 0.5521 Acc: 0.7124\n",
      "has spend time 43m 5s/n\n",
      "\n",
      "Epoch 1209/9999\n",
      "----------\n",
      "train Loss: 0.4962 Acc: 0.7541\n",
      "has spend time 43m 7s/n\n",
      "val Loss: 0.5498 Acc: 0.7059\n",
      "has spend time 43m 7s/n\n",
      "\n",
      "Epoch 1210/9999\n",
      "----------\n",
      "train Loss: 0.4916 Acc: 0.7582\n",
      "has spend time 43m 9s/n\n",
      "val Loss: 0.5504 Acc: 0.7059\n",
      "has spend time 43m 9s/n\n",
      "\n",
      "Epoch 1211/9999\n",
      "----------\n",
      "train Loss: 0.5316 Acc: 0.7295\n",
      "has spend time 43m 11s/n\n",
      "val Loss: 0.5540 Acc: 0.7124\n",
      "has spend time 43m 11s/n\n",
      "\n",
      "Epoch 1212/9999\n",
      "----------\n",
      "train Loss: 0.5275 Acc: 0.7049\n",
      "has spend time 43m 13s/n\n",
      "val Loss: 0.5746 Acc: 0.6928\n",
      "has spend time 43m 13s/n\n",
      "\n",
      "Epoch 1213/9999\n",
      "----------\n",
      "train Loss: 0.4967 Acc: 0.7705\n",
      "has spend time 43m 15s/n\n",
      "val Loss: 0.5773 Acc: 0.6928\n",
      "has spend time 43m 15s/n\n",
      "\n",
      "Epoch 1214/9999\n",
      "----------\n",
      "train Loss: 0.4954 Acc: 0.7623\n",
      "has spend time 43m 17s/n\n",
      "val Loss: 0.5543 Acc: 0.6993\n",
      "has spend time 43m 18s/n\n",
      "\n",
      "Epoch 1215/9999\n",
      "----------\n",
      "train Loss: 0.5075 Acc: 0.7459\n",
      "has spend time 43m 19s/n\n",
      "val Loss: 0.5491 Acc: 0.7059\n",
      "has spend time 43m 20s/n\n",
      "\n",
      "Epoch 1216/9999\n",
      "----------\n",
      "train Loss: 0.5128 Acc: 0.7090\n",
      "has spend time 43m 21s/n\n",
      "val Loss: 0.5471 Acc: 0.7124\n",
      "has spend time 43m 22s/n\n",
      "\n",
      "Epoch 1217/9999\n",
      "----------\n",
      "train Loss: 0.5069 Acc: 0.7336\n",
      "has spend time 43m 23s/n\n",
      "val Loss: 0.5473 Acc: 0.7124\n",
      "has spend time 43m 24s/n\n",
      "\n",
      "Epoch 1218/9999\n",
      "----------\n",
      "train Loss: 0.5000 Acc: 0.7746\n",
      "has spend time 43m 25s/n\n",
      "val Loss: 0.5492 Acc: 0.7059\n",
      "has spend time 43m 26s/n\n",
      "\n",
      "Epoch 1219/9999\n",
      "----------\n",
      "train Loss: 0.5481 Acc: 0.7008\n",
      "has spend time 43m 27s/n\n",
      "val Loss: 0.5452 Acc: 0.7124\n",
      "has spend time 43m 28s/n\n",
      "\n",
      "Epoch 1220/9999\n",
      "----------\n",
      "train Loss: 0.4931 Acc: 0.7295\n",
      "has spend time 43m 30s/n\n",
      "val Loss: 0.5448 Acc: 0.6993\n",
      "has spend time 43m 31s/n\n",
      "\n",
      "Epoch 1221/9999\n",
      "----------\n",
      "train Loss: 0.4975 Acc: 0.7418\n",
      "has spend time 43m 32s/n\n",
      "val Loss: 0.5435 Acc: 0.7124\n",
      "has spend time 43m 33s/n\n",
      "\n",
      "Epoch 1222/9999\n",
      "----------\n",
      "train Loss: 0.5346 Acc: 0.7131\n",
      "has spend time 43m 34s/n\n",
      "val Loss: 0.5631 Acc: 0.6928\n",
      "has spend time 43m 35s/n\n",
      "\n",
      "Epoch 1223/9999\n",
      "----------\n",
      "train Loss: 0.5475 Acc: 0.7336\n",
      "has spend time 43m 36s/n\n",
      "val Loss: 0.5474 Acc: 0.7059\n",
      "has spend time 43m 37s/n\n",
      "\n",
      "Epoch 1224/9999\n",
      "----------\n",
      "train Loss: 0.4950 Acc: 0.7541\n",
      "has spend time 43m 39s/n\n",
      "val Loss: 0.5568 Acc: 0.6928\n",
      "has spend time 43m 39s/n\n",
      "\n",
      "Epoch 1225/9999\n",
      "----------\n",
      "train Loss: 0.5267 Acc: 0.7336\n",
      "has spend time 43m 41s/n\n",
      "val Loss: 0.5637 Acc: 0.7059\n",
      "has spend time 43m 41s/n\n",
      "\n",
      "Epoch 1226/9999\n",
      "----------\n",
      "train Loss: 0.5166 Acc: 0.7049\n",
      "has spend time 43m 43s/n\n",
      "val Loss: 0.5550 Acc: 0.6928\n",
      "has spend time 43m 43s/n\n",
      "\n",
      "Epoch 1227/9999\n",
      "----------\n",
      "train Loss: 0.5191 Acc: 0.7377\n",
      "has spend time 43m 45s/n\n",
      "val Loss: 0.5590 Acc: 0.7059\n",
      "has spend time 43m 46s/n\n",
      "\n",
      "Epoch 1228/9999\n",
      "----------\n",
      "train Loss: 0.5075 Acc: 0.7377\n",
      "has spend time 43m 47s/n\n",
      "val Loss: 0.5433 Acc: 0.7190\n",
      "has spend time 43m 48s/n\n",
      "\n",
      "Epoch 1229/9999\n",
      "----------\n",
      "train Loss: 0.5248 Acc: 0.7172\n",
      "has spend time 43m 49s/n\n",
      "val Loss: 0.5441 Acc: 0.7190\n",
      "has spend time 43m 50s/n\n",
      "\n",
      "Epoch 1230/9999\n",
      "----------\n",
      "train Loss: 0.4883 Acc: 0.7705\n",
      "has spend time 43m 52s/n\n",
      "val Loss: 0.5511 Acc: 0.7190\n",
      "has spend time 43m 52s/n\n",
      "\n",
      "Epoch 1231/9999\n",
      "----------\n",
      "train Loss: 0.4861 Acc: 0.7664\n",
      "has spend time 43m 54s/n\n",
      "val Loss: 0.5492 Acc: 0.7124\n",
      "has spend time 43m 55s/n\n",
      "\n",
      "Epoch 1232/9999\n",
      "----------\n",
      "train Loss: 0.5276 Acc: 0.7049\n",
      "has spend time 43m 56s/n\n",
      "val Loss: 0.5481 Acc: 0.7124\n",
      "has spend time 43m 57s/n\n",
      "\n",
      "Epoch 1233/9999\n",
      "----------\n",
      "train Loss: 0.4852 Acc: 0.7705\n",
      "has spend time 43m 58s/n\n",
      "val Loss: 0.5506 Acc: 0.7124\n",
      "has spend time 43m 59s/n\n",
      "\n",
      "Epoch 1234/9999\n",
      "----------\n",
      "train Loss: 0.4739 Acc: 0.7869\n",
      "has spend time 44m 0s/n\n",
      "val Loss: 0.5810 Acc: 0.6928\n",
      "has spend time 44m 1s/n\n",
      "\n",
      "Epoch 1235/9999\n",
      "----------\n",
      "train Loss: 0.5123 Acc: 0.7172\n",
      "has spend time 44m 2s/n\n",
      "val Loss: 0.5529 Acc: 0.7124\n",
      "has spend time 44m 3s/n\n",
      "\n",
      "Epoch 1236/9999\n",
      "----------\n",
      "train Loss: 0.5214 Acc: 0.7500\n",
      "has spend time 44m 4s/n\n",
      "val Loss: 0.5482 Acc: 0.7059\n",
      "has spend time 44m 5s/n\n",
      "\n",
      "Epoch 1237/9999\n",
      "----------\n",
      "train Loss: 0.4911 Acc: 0.7746\n",
      "has spend time 44m 7s/n\n",
      "val Loss: 0.5519 Acc: 0.7190\n",
      "has spend time 44m 7s/n\n",
      "\n",
      "Epoch 1238/9999\n",
      "----------\n",
      "train Loss: 0.5035 Acc: 0.7500\n",
      "has spend time 44m 9s/n\n",
      "val Loss: 0.5732 Acc: 0.6928\n",
      "has spend time 44m 9s/n\n",
      "\n",
      "Epoch 1239/9999\n",
      "----------\n",
      "train Loss: 0.5626 Acc: 0.6926\n",
      "has spend time 44m 11s/n\n",
      "val Loss: 0.5516 Acc: 0.7190\n",
      "has spend time 44m 11s/n\n",
      "\n",
      "Epoch 1240/9999\n",
      "----------\n",
      "train Loss: 0.5274 Acc: 0.7336\n",
      "has spend time 44m 13s/n\n",
      "val Loss: 0.5495 Acc: 0.7124\n",
      "has spend time 44m 13s/n\n",
      "\n",
      "Epoch 1241/9999\n",
      "----------\n",
      "train Loss: 0.5109 Acc: 0.7459\n",
      "has spend time 44m 15s/n\n",
      "val Loss: 0.5442 Acc: 0.7059\n",
      "has spend time 44m 16s/n\n",
      "\n",
      "Epoch 1242/9999\n",
      "----------\n",
      "train Loss: 0.4820 Acc: 0.7664\n",
      "has spend time 44m 17s/n\n",
      "val Loss: 0.5520 Acc: 0.7059\n",
      "has spend time 44m 18s/n\n",
      "\n",
      "Epoch 1243/9999\n",
      "----------\n",
      "train Loss: 0.4947 Acc: 0.7254\n",
      "has spend time 44m 19s/n\n",
      "val Loss: 0.5488 Acc: 0.7190\n",
      "has spend time 44m 20s/n\n",
      "\n",
      "Epoch 1244/9999\n",
      "----------\n",
      "train Loss: 0.5066 Acc: 0.7541\n",
      "has spend time 44m 21s/n\n",
      "val Loss: 0.5457 Acc: 0.7190\n",
      "has spend time 44m 22s/n\n",
      "\n",
      "Epoch 1245/9999\n",
      "----------\n",
      "train Loss: 0.4886 Acc: 0.7541\n",
      "has spend time 44m 23s/n\n",
      "val Loss: 0.5529 Acc: 0.6863\n",
      "has spend time 44m 24s/n\n",
      "\n",
      "Epoch 1246/9999\n",
      "----------\n",
      "train Loss: 0.5392 Acc: 0.6967\n",
      "has spend time 44m 26s/n\n",
      "val Loss: 0.5542 Acc: 0.6993\n",
      "has spend time 44m 26s/n\n",
      "\n",
      "Epoch 1247/9999\n",
      "----------\n",
      "train Loss: 0.5047 Acc: 0.7213\n",
      "has spend time 44m 28s/n\n",
      "val Loss: 0.5636 Acc: 0.6863\n",
      "has spend time 44m 29s/n\n",
      "\n",
      "Epoch 1248/9999\n",
      "----------\n",
      "train Loss: 0.5090 Acc: 0.7377\n",
      "has spend time 44m 30s/n\n",
      "val Loss: 0.5623 Acc: 0.6928\n",
      "has spend time 44m 31s/n\n",
      "\n",
      "Epoch 1249/9999\n",
      "----------\n",
      "train Loss: 0.4966 Acc: 0.7459\n",
      "has spend time 44m 32s/n\n",
      "val Loss: 0.5557 Acc: 0.6993\n",
      "has spend time 44m 33s/n\n",
      "\n",
      "Epoch 1250/9999\n",
      "----------\n",
      "train Loss: 0.5065 Acc: 0.7500\n",
      "has spend time 44m 34s/n\n",
      "val Loss: 0.5488 Acc: 0.7124\n",
      "has spend time 44m 35s/n\n",
      "\n",
      "Epoch 1251/9999\n",
      "----------\n",
      "train Loss: 0.5067 Acc: 0.7582\n",
      "has spend time 44m 36s/n\n",
      "val Loss: 0.5438 Acc: 0.7059\n",
      "has spend time 44m 37s/n\n",
      "\n",
      "Epoch 1252/9999\n",
      "----------\n",
      "train Loss: 0.5091 Acc: 0.7418\n",
      "has spend time 44m 39s/n\n",
      "val Loss: 0.5544 Acc: 0.7124\n",
      "has spend time 44m 39s/n\n",
      "\n",
      "Epoch 1253/9999\n",
      "----------\n",
      "train Loss: 0.5192 Acc: 0.7254\n",
      "has spend time 44m 41s/n\n",
      "val Loss: 0.5556 Acc: 0.7124\n",
      "has spend time 44m 41s/n\n",
      "\n",
      "Epoch 1254/9999\n",
      "----------\n",
      "train Loss: 0.4942 Acc: 0.7582\n",
      "has spend time 44m 43s/n\n",
      "val Loss: 0.5460 Acc: 0.7190\n",
      "has spend time 44m 44s/n\n",
      "\n",
      "Epoch 1255/9999\n",
      "----------\n",
      "train Loss: 0.5027 Acc: 0.7541\n",
      "has spend time 44m 45s/n\n",
      "val Loss: 0.5649 Acc: 0.6928\n",
      "has spend time 44m 46s/n\n",
      "\n",
      "Epoch 1256/9999\n",
      "----------\n",
      "train Loss: 0.5192 Acc: 0.7459\n",
      "has spend time 44m 47s/n\n",
      "val Loss: 0.5587 Acc: 0.6928\n",
      "has spend time 44m 48s/n\n",
      "\n",
      "Epoch 1257/9999\n",
      "----------\n",
      "train Loss: 0.5058 Acc: 0.7541\n",
      "has spend time 44m 49s/n\n",
      "val Loss: 0.5627 Acc: 0.6993\n",
      "has spend time 44m 50s/n\n",
      "\n",
      "Epoch 1258/9999\n",
      "----------\n",
      "train Loss: 0.5000 Acc: 0.7418\n",
      "has spend time 44m 51s/n\n",
      "val Loss: 0.5494 Acc: 0.7190\n",
      "has spend time 44m 52s/n\n",
      "\n",
      "Epoch 1259/9999\n",
      "----------\n",
      "train Loss: 0.5012 Acc: 0.7377\n",
      "has spend time 44m 53s/n\n",
      "val Loss: 0.5643 Acc: 0.6993\n",
      "has spend time 44m 54s/n\n",
      "\n",
      "Epoch 1260/9999\n",
      "----------\n",
      "train Loss: 0.5007 Acc: 0.7336\n",
      "has spend time 44m 55s/n\n",
      "val Loss: 0.5557 Acc: 0.6993\n",
      "has spend time 44m 56s/n\n",
      "\n",
      "Epoch 1261/9999\n",
      "----------\n",
      "train Loss: 0.5237 Acc: 0.7295\n",
      "has spend time 44m 58s/n\n",
      "val Loss: 0.5637 Acc: 0.6993\n",
      "has spend time 44m 58s/n\n",
      "\n",
      "Epoch 1262/9999\n",
      "----------\n",
      "train Loss: 0.5203 Acc: 0.7295\n",
      "has spend time 44m 60s/n\n",
      "val Loss: 0.5461 Acc: 0.7124\n",
      "has spend time 45m 1s/n\n",
      "\n",
      "Epoch 1263/9999\n",
      "----------\n",
      "train Loss: 0.5021 Acc: 0.7336\n",
      "has spend time 45m 2s/n\n",
      "val Loss: 0.5508 Acc: 0.7124\n",
      "has spend time 45m 3s/n\n",
      "\n",
      "Epoch 1264/9999\n",
      "----------\n",
      "train Loss: 0.5254 Acc: 0.7172\n",
      "has spend time 45m 4s/n\n",
      "val Loss: 0.5590 Acc: 0.7059\n",
      "has spend time 45m 5s/n\n",
      "\n",
      "Epoch 1265/9999\n",
      "----------\n",
      "train Loss: 0.5149 Acc: 0.7254\n",
      "has spend time 45m 6s/n\n",
      "val Loss: 0.5622 Acc: 0.6993\n",
      "has spend time 45m 7s/n\n",
      "\n",
      "Epoch 1266/9999\n",
      "----------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train Loss: 0.5247 Acc: 0.7213\n",
      "has spend time 45m 8s/n\n",
      "val Loss: 0.5481 Acc: 0.7059\n",
      "has spend time 45m 9s/n\n",
      "\n",
      "Epoch 1267/9999\n",
      "----------\n",
      "train Loss: 0.4964 Acc: 0.7377\n",
      "has spend time 45m 10s/n\n",
      "val Loss: 0.5532 Acc: 0.6928\n",
      "has spend time 45m 11s/n\n",
      "\n",
      "Epoch 1268/9999\n",
      "----------\n",
      "train Loss: 0.5292 Acc: 0.7172\n",
      "has spend time 45m 13s/n\n",
      "val Loss: 0.5596 Acc: 0.7059\n",
      "has spend time 45m 13s/n\n",
      "\n",
      "Epoch 1269/9999\n",
      "----------\n",
      "train Loss: 0.5403 Acc: 0.7172\n",
      "has spend time 45m 15s/n\n",
      "val Loss: 0.5522 Acc: 0.7059\n",
      "has spend time 45m 15s/n\n",
      "\n",
      "Epoch 1270/9999\n",
      "----------\n",
      "train Loss: 0.5027 Acc: 0.7254\n",
      "has spend time 45m 17s/n\n",
      "val Loss: 0.5432 Acc: 0.7255\n",
      "has spend time 45m 17s/n\n",
      "\n",
      "Epoch 1271/9999\n",
      "----------\n",
      "train Loss: 0.5173 Acc: 0.7213\n",
      "has spend time 45m 19s/n\n",
      "val Loss: 0.5441 Acc: 0.7059\n",
      "has spend time 45m 19s/n\n",
      "\n",
      "Epoch 1272/9999\n",
      "----------\n",
      "train Loss: 0.4939 Acc: 0.7418\n",
      "has spend time 45m 21s/n\n",
      "val Loss: 0.5514 Acc: 0.7190\n",
      "has spend time 45m 21s/n\n",
      "\n",
      "Epoch 1273/9999\n",
      "----------\n",
      "train Loss: 0.5082 Acc: 0.7336\n",
      "has spend time 45m 23s/n\n",
      "val Loss: 0.5479 Acc: 0.7124\n",
      "has spend time 45m 23s/n\n",
      "\n",
      "Epoch 1274/9999\n",
      "----------\n",
      "train Loss: 0.4995 Acc: 0.7295\n",
      "has spend time 45m 25s/n\n",
      "val Loss: 0.5522 Acc: 0.7124\n",
      "has spend time 45m 25s/n\n",
      "\n",
      "Epoch 1275/9999\n",
      "----------\n",
      "train Loss: 0.5527 Acc: 0.7008\n",
      "has spend time 45m 27s/n\n",
      "val Loss: 0.5468 Acc: 0.7124\n",
      "has spend time 45m 27s/n\n",
      "\n",
      "Epoch 1276/9999\n",
      "----------\n",
      "train Loss: 0.5048 Acc: 0.7254\n",
      "has spend time 45m 29s/n\n",
      "val Loss: 0.5460 Acc: 0.6993\n",
      "has spend time 45m 29s/n\n",
      "\n",
      "Epoch 1277/9999\n",
      "----------\n",
      "train Loss: 0.4916 Acc: 0.7541\n",
      "has spend time 45m 31s/n\n",
      "val Loss: 0.5466 Acc: 0.7190\n",
      "has spend time 45m 32s/n\n",
      "\n",
      "Epoch 1278/9999\n",
      "----------\n",
      "train Loss: 0.5061 Acc: 0.7459\n",
      "has spend time 45m 33s/n\n",
      "val Loss: 0.5444 Acc: 0.7255\n",
      "has spend time 45m 34s/n\n",
      "\n",
      "Epoch 1279/9999\n",
      "----------\n",
      "train Loss: 0.5304 Acc: 0.7295\n",
      "has spend time 45m 36s/n\n",
      "val Loss: 0.5492 Acc: 0.7059\n",
      "has spend time 45m 36s/n\n",
      "\n",
      "Epoch 1280/9999\n",
      "----------\n",
      "train Loss: 0.5187 Acc: 0.7459\n",
      "has spend time 45m 38s/n\n",
      "val Loss: 0.5515 Acc: 0.7190\n",
      "has spend time 45m 38s/n\n",
      "\n",
      "Epoch 1281/9999\n",
      "----------\n",
      "train Loss: 0.4973 Acc: 0.7664\n",
      "has spend time 45m 40s/n\n",
      "val Loss: 0.5628 Acc: 0.6928\n",
      "has spend time 45m 40s/n\n",
      "\n",
      "Epoch 1282/9999\n",
      "----------\n",
      "train Loss: 0.4892 Acc: 0.7459\n",
      "has spend time 45m 42s/n\n",
      "val Loss: 0.5469 Acc: 0.7059\n",
      "has spend time 45m 42s/n\n",
      "\n",
      "Epoch 1283/9999\n",
      "----------\n",
      "train Loss: 0.5466 Acc: 0.7418\n",
      "has spend time 45m 44s/n\n",
      "val Loss: 0.5480 Acc: 0.7255\n",
      "has spend time 45m 44s/n\n",
      "\n",
      "Epoch 1284/9999\n",
      "----------\n",
      "train Loss: 0.5259 Acc: 0.7295\n",
      "has spend time 45m 46s/n\n",
      "val Loss: 0.5503 Acc: 0.6928\n",
      "has spend time 45m 47s/n\n",
      "\n",
      "Epoch 1285/9999\n",
      "----------\n",
      "train Loss: 0.5218 Acc: 0.7172\n",
      "has spend time 45m 48s/n\n",
      "val Loss: 0.5530 Acc: 0.7124\n",
      "has spend time 45m 49s/n\n",
      "\n",
      "Epoch 1286/9999\n",
      "----------\n",
      "train Loss: 0.5450 Acc: 0.6598\n",
      "has spend time 45m 50s/n\n",
      "val Loss: 0.5482 Acc: 0.6993\n",
      "has spend time 45m 51s/n\n",
      "\n",
      "Epoch 1287/9999\n",
      "----------\n",
      "train Loss: 0.4873 Acc: 0.7500\n",
      "has spend time 45m 52s/n\n",
      "val Loss: 0.5669 Acc: 0.6928\n",
      "has spend time 45m 53s/n\n",
      "\n",
      "Epoch 1288/9999\n",
      "----------\n",
      "train Loss: 0.5317 Acc: 0.7336\n",
      "has spend time 45m 54s/n\n",
      "val Loss: 0.5497 Acc: 0.7059\n",
      "has spend time 45m 55s/n\n",
      "\n",
      "Epoch 1289/9999\n",
      "----------\n",
      "train Loss: 0.4943 Acc: 0.7500\n",
      "has spend time 45m 56s/n\n",
      "val Loss: 0.5448 Acc: 0.7124\n",
      "has spend time 45m 57s/n\n",
      "\n",
      "Epoch 1290/9999\n",
      "----------\n",
      "train Loss: 0.4975 Acc: 0.7664\n",
      "has spend time 45m 58s/n\n",
      "val Loss: 0.5513 Acc: 0.6993\n",
      "has spend time 45m 59s/n\n",
      "\n",
      "Epoch 1291/9999\n",
      "----------\n",
      "train Loss: 0.5207 Acc: 0.7336\n",
      "has spend time 46m 1s/n\n",
      "val Loss: 0.5436 Acc: 0.7124\n",
      "has spend time 46m 1s/n\n",
      "\n",
      "Epoch 1292/9999\n",
      "----------\n",
      "train Loss: 0.5097 Acc: 0.7336\n",
      "has spend time 46m 3s/n\n",
      "val Loss: 0.5474 Acc: 0.7124\n",
      "has spend time 46m 4s/n\n",
      "\n",
      "Epoch 1293/9999\n",
      "----------\n",
      "train Loss: 0.4977 Acc: 0.7418\n",
      "has spend time 46m 5s/n\n",
      "val Loss: 0.5633 Acc: 0.6993\n",
      "has spend time 46m 6s/n\n",
      "\n",
      "Epoch 1294/9999\n",
      "----------\n",
      "train Loss: 0.4891 Acc: 0.7664\n",
      "has spend time 46m 7s/n\n",
      "val Loss: 0.5519 Acc: 0.7059\n",
      "has spend time 46m 8s/n\n",
      "\n",
      "Epoch 1295/9999\n",
      "----------\n",
      "train Loss: 0.5335 Acc: 0.7131\n",
      "has spend time 46m 9s/n\n",
      "val Loss: 0.5669 Acc: 0.6928\n",
      "has spend time 46m 10s/n\n",
      "\n",
      "Epoch 1296/9999\n",
      "----------\n",
      "train Loss: 0.5288 Acc: 0.7336\n",
      "has spend time 46m 11s/n\n",
      "val Loss: 0.5815 Acc: 0.6928\n",
      "has spend time 46m 12s/n\n",
      "\n",
      "Epoch 1297/9999\n",
      "----------\n",
      "train Loss: 0.4959 Acc: 0.7418\n",
      "has spend time 46m 13s/n\n",
      "val Loss: 0.5485 Acc: 0.7190\n",
      "has spend time 46m 14s/n\n",
      "\n",
      "Epoch 1298/9999\n",
      "----------\n",
      "train Loss: 0.4843 Acc: 0.7787\n",
      "has spend time 46m 15s/n\n",
      "val Loss: 0.5565 Acc: 0.6993\n",
      "has spend time 46m 16s/n\n",
      "\n",
      "Epoch 1299/9999\n",
      "----------\n",
      "train Loss: 0.5050 Acc: 0.7459\n",
      "has spend time 46m 18s/n\n",
      "val Loss: 0.5413 Acc: 0.7190\n",
      "has spend time 46m 18s/n\n",
      "\n",
      "Epoch 1300/9999\n",
      "----------\n",
      "train Loss: 0.5137 Acc: 0.7254\n",
      "has spend time 46m 20s/n\n",
      "val Loss: 0.5479 Acc: 0.7124\n",
      "has spend time 46m 21s/n\n",
      "\n",
      "Epoch 1301/9999\n",
      "----------\n",
      "train Loss: 0.5265 Acc: 0.7623\n",
      "has spend time 46m 22s/n\n",
      "val Loss: 0.5723 Acc: 0.6928\n",
      "has spend time 46m 23s/n\n",
      "\n",
      "Epoch 1302/9999\n",
      "----------\n",
      "train Loss: 0.5143 Acc: 0.7541\n",
      "has spend time 46m 24s/n\n",
      "val Loss: 0.5639 Acc: 0.6993\n",
      "has spend time 46m 25s/n\n",
      "\n",
      "Epoch 1303/9999\n",
      "----------\n",
      "train Loss: 0.5106 Acc: 0.7664\n",
      "has spend time 46m 26s/n\n",
      "val Loss: 0.5503 Acc: 0.7124\n",
      "has spend time 46m 27s/n\n",
      "\n",
      "Epoch 1304/9999\n",
      "----------\n",
      "train Loss: 0.5146 Acc: 0.7049\n",
      "has spend time 46m 28s/n\n",
      "val Loss: 0.5614 Acc: 0.7059\n",
      "has spend time 46m 29s/n\n",
      "\n",
      "Epoch 1305/9999\n",
      "----------\n",
      "train Loss: 0.5383 Acc: 0.7090\n",
      "has spend time 46m 31s/n\n",
      "val Loss: 0.5518 Acc: 0.7124\n",
      "has spend time 46m 32s/n\n",
      "\n",
      "Epoch 1306/9999\n",
      "----------\n",
      "train Loss: 0.5017 Acc: 0.7500\n",
      "has spend time 46m 33s/n\n",
      "val Loss: 0.5598 Acc: 0.7059\n",
      "has spend time 46m 34s/n\n",
      "\n",
      "Epoch 1307/9999\n",
      "----------\n",
      "train Loss: 0.5016 Acc: 0.7541\n",
      "has spend time 46m 35s/n\n",
      "val Loss: 0.5603 Acc: 0.6928\n",
      "has spend time 46m 36s/n\n",
      "\n",
      "Epoch 1308/9999\n",
      "----------\n",
      "train Loss: 0.4781 Acc: 0.7459\n",
      "has spend time 46m 37s/n\n",
      "val Loss: 0.5456 Acc: 0.7190\n",
      "has spend time 46m 38s/n\n",
      "\n",
      "Epoch 1309/9999\n",
      "----------\n",
      "train Loss: 0.5315 Acc: 0.7090\n",
      "has spend time 46m 39s/n\n",
      "val Loss: 0.5474 Acc: 0.7124\n",
      "has spend time 46m 40s/n\n",
      "\n",
      "Epoch 1310/9999\n",
      "----------\n",
      "train Loss: 0.4771 Acc: 0.7623\n",
      "has spend time 46m 41s/n\n",
      "val Loss: 0.5400 Acc: 0.7190\n",
      "has spend time 46m 42s/n\n",
      "\n",
      "Epoch 1311/9999\n",
      "----------\n",
      "train Loss: 0.5258 Acc: 0.7131\n",
      "has spend time 46m 44s/n\n",
      "val Loss: 0.5536 Acc: 0.6993\n",
      "has spend time 46m 44s/n\n",
      "\n",
      "Epoch 1312/9999\n",
      "----------\n",
      "train Loss: 0.5383 Acc: 0.6639\n",
      "has spend time 46m 46s/n\n",
      "val Loss: 0.5461 Acc: 0.7059\n",
      "has spend time 46m 46s/n\n",
      "\n",
      "Epoch 1313/9999\n",
      "----------\n",
      "train Loss: 0.5291 Acc: 0.7090\n",
      "has spend time 46m 48s/n\n",
      "val Loss: 0.5607 Acc: 0.7059\n",
      "has spend time 46m 48s/n\n",
      "\n",
      "Epoch 1314/9999\n",
      "----------\n",
      "train Loss: 0.4824 Acc: 0.7705\n",
      "has spend time 46m 50s/n\n",
      "val Loss: 0.5481 Acc: 0.7190\n",
      "has spend time 46m 50s/n\n",
      "\n",
      "Epoch 1315/9999\n",
      "----------\n",
      "train Loss: 0.5082 Acc: 0.7336\n",
      "has spend time 46m 52s/n\n",
      "val Loss: 0.5542 Acc: 0.7059\n",
      "has spend time 46m 53s/n\n",
      "\n",
      "Epoch 1316/9999\n",
      "----------\n",
      "train Loss: 0.5050 Acc: 0.7582\n",
      "has spend time 46m 54s/n\n",
      "val Loss: 0.5463 Acc: 0.7255\n",
      "has spend time 46m 55s/n\n",
      "\n",
      "Epoch 1317/9999\n",
      "----------\n",
      "train Loss: 0.4929 Acc: 0.7541\n",
      "has spend time 46m 56s/n\n",
      "val Loss: 0.5445 Acc: 0.7190\n",
      "has spend time 46m 57s/n\n",
      "\n",
      "Epoch 1318/9999\n",
      "----------\n",
      "train Loss: 0.5267 Acc: 0.7090\n",
      "has spend time 46m 58s/n\n",
      "val Loss: 0.5572 Acc: 0.7059\n",
      "has spend time 46m 59s/n\n",
      "\n",
      "Epoch 1319/9999\n",
      "----------\n",
      "train Loss: 0.5009 Acc: 0.7254\n",
      "has spend time 47m 0s/n\n",
      "val Loss: 0.5482 Acc: 0.7190\n",
      "has spend time 47m 1s/n\n",
      "\n",
      "Epoch 1320/9999\n",
      "----------\n",
      "train Loss: 0.4760 Acc: 0.7828\n",
      "has spend time 47m 2s/n\n",
      "val Loss: 0.5595 Acc: 0.6993\n",
      "has spend time 47m 3s/n\n",
      "\n",
      "Epoch 1321/9999\n",
      "----------\n",
      "train Loss: 0.5262 Acc: 0.7500\n",
      "has spend time 47m 4s/n\n",
      "val Loss: 0.5609 Acc: 0.7059\n",
      "has spend time 47m 5s/n\n",
      "\n",
      "Epoch 1322/9999\n",
      "----------\n",
      "train Loss: 0.5196 Acc: 0.7131\n",
      "has spend time 47m 6s/n\n",
      "val Loss: 0.5485 Acc: 0.7059\n",
      "has spend time 47m 7s/n\n",
      "\n",
      "Epoch 1323/9999\n",
      "----------\n",
      "train Loss: 0.5226 Acc: 0.7213\n",
      "has spend time 47m 8s/n\n",
      "val Loss: 0.5539 Acc: 0.7190\n",
      "has spend time 47m 9s/n\n",
      "\n",
      "Epoch 1324/9999\n",
      "----------\n",
      "train Loss: 0.5156 Acc: 0.7090\n",
      "has spend time 47m 11s/n\n",
      "val Loss: 0.5548 Acc: 0.7124\n",
      "has spend time 47m 12s/n\n",
      "\n",
      "Epoch 1325/9999\n",
      "----------\n",
      "train Loss: 0.5213 Acc: 0.7172\n",
      "has spend time 47m 13s/n\n",
      "val Loss: 0.5503 Acc: 0.7190\n",
      "has spend time 47m 14s/n\n",
      "\n",
      "Epoch 1326/9999\n",
      "----------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train Loss: 0.5077 Acc: 0.7254\n",
      "has spend time 47m 15s/n\n",
      "val Loss: 0.5405 Acc: 0.7255\n",
      "has spend time 47m 16s/n\n",
      "\n",
      "Epoch 1327/9999\n",
      "----------\n",
      "train Loss: 0.5208 Acc: 0.7213\n",
      "has spend time 47m 17s/n\n",
      "val Loss: 0.5478 Acc: 0.7190\n",
      "has spend time 47m 18s/n\n",
      "\n",
      "Epoch 1328/9999\n",
      "----------\n",
      "train Loss: 0.4756 Acc: 0.7828\n",
      "has spend time 47m 20s/n\n",
      "val Loss: 0.5510 Acc: 0.6993\n",
      "has spend time 47m 20s/n\n",
      "\n",
      "Epoch 1329/9999\n",
      "----------\n",
      "train Loss: 0.5235 Acc: 0.7295\n",
      "has spend time 47m 22s/n\n",
      "val Loss: 0.5471 Acc: 0.6993\n",
      "has spend time 47m 22s/n\n",
      "\n",
      "Epoch 1330/9999\n",
      "----------\n",
      "train Loss: 0.5213 Acc: 0.7131\n",
      "has spend time 47m 24s/n\n",
      "val Loss: 0.5566 Acc: 0.6928\n",
      "has spend time 47m 24s/n\n",
      "\n",
      "Epoch 1331/9999\n",
      "----------\n",
      "train Loss: 0.4717 Acc: 0.7746\n",
      "has spend time 47m 26s/n\n",
      "val Loss: 0.5483 Acc: 0.7124\n",
      "has spend time 47m 26s/n\n",
      "\n",
      "Epoch 1332/9999\n",
      "----------\n",
      "train Loss: 0.5015 Acc: 0.7254\n",
      "has spend time 47m 28s/n\n",
      "val Loss: 0.5536 Acc: 0.7059\n",
      "has spend time 47m 28s/n\n",
      "\n",
      "Epoch 1333/9999\n",
      "----------\n",
      "train Loss: 0.4908 Acc: 0.7459\n",
      "has spend time 47m 30s/n\n",
      "val Loss: 0.5536 Acc: 0.6928\n",
      "has spend time 47m 30s/n\n",
      "\n",
      "Epoch 1334/9999\n",
      "----------\n",
      "train Loss: 0.4833 Acc: 0.7623\n",
      "has spend time 47m 32s/n\n",
      "val Loss: 0.5428 Acc: 0.7124\n",
      "has spend time 47m 32s/n\n",
      "\n",
      "Epoch 1335/9999\n",
      "----------\n",
      "train Loss: 0.5095 Acc: 0.7336\n",
      "has spend time 47m 34s/n\n",
      "val Loss: 0.5437 Acc: 0.7190\n",
      "has spend time 47m 34s/n\n",
      "\n",
      "Epoch 1336/9999\n",
      "----------\n",
      "train Loss: 0.5183 Acc: 0.7582\n",
      "has spend time 47m 36s/n\n",
      "val Loss: 0.5497 Acc: 0.7190\n",
      "has spend time 47m 37s/n\n",
      "\n",
      "Epoch 1337/9999\n",
      "----------\n",
      "train Loss: 0.5069 Acc: 0.7377\n",
      "has spend time 47m 38s/n\n",
      "val Loss: 0.5456 Acc: 0.7190\n",
      "has spend time 47m 39s/n\n",
      "\n",
      "Epoch 1338/9999\n",
      "----------\n",
      "train Loss: 0.4950 Acc: 0.7295\n",
      "has spend time 47m 40s/n\n",
      "val Loss: 0.5406 Acc: 0.7255\n",
      "has spend time 47m 41s/n\n",
      "\n",
      "Epoch 1339/9999\n",
      "----------\n",
      "train Loss: 0.5193 Acc: 0.7336\n",
      "has spend time 47m 42s/n\n",
      "val Loss: 0.5475 Acc: 0.7124\n",
      "has spend time 47m 43s/n\n",
      "\n",
      "Epoch 1340/9999\n",
      "----------\n",
      "train Loss: 0.5242 Acc: 0.7623\n",
      "has spend time 47m 44s/n\n",
      "val Loss: 0.5525 Acc: 0.7255\n",
      "has spend time 47m 45s/n\n",
      "\n",
      "Epoch 1341/9999\n",
      "----------\n",
      "train Loss: 0.5120 Acc: 0.7295\n",
      "has spend time 47m 47s/n\n",
      "val Loss: 0.5528 Acc: 0.7059\n",
      "has spend time 47m 47s/n\n",
      "\n",
      "Epoch 1342/9999\n",
      "----------\n",
      "train Loss: 0.4825 Acc: 0.7418\n",
      "has spend time 47m 49s/n\n",
      "val Loss: 0.5486 Acc: 0.7124\n",
      "has spend time 47m 49s/n\n",
      "\n",
      "Epoch 1343/9999\n",
      "----------\n",
      "train Loss: 0.4997 Acc: 0.7582\n",
      "has spend time 47m 51s/n\n",
      "val Loss: 0.5485 Acc: 0.7124\n",
      "has spend time 47m 52s/n\n",
      "\n",
      "Epoch 1344/9999\n",
      "----------\n",
      "train Loss: 0.5073 Acc: 0.7336\n",
      "has spend time 47m 53s/n\n",
      "val Loss: 0.5703 Acc: 0.6928\n",
      "has spend time 47m 54s/n\n",
      "\n",
      "Epoch 1345/9999\n",
      "----------\n",
      "train Loss: 0.5301 Acc: 0.7213\n",
      "has spend time 47m 56s/n\n",
      "val Loss: 0.5448 Acc: 0.7124\n",
      "has spend time 47m 56s/n\n",
      "\n",
      "Epoch 1346/9999\n",
      "----------\n",
      "train Loss: 0.5009 Acc: 0.7213\n",
      "has spend time 47m 58s/n\n",
      "val Loss: 0.5424 Acc: 0.7255\n",
      "has spend time 47m 58s/n\n",
      "\n",
      "Epoch 1347/9999\n",
      "----------\n",
      "train Loss: 0.4973 Acc: 0.7336\n",
      "has spend time 47m 60s/n\n",
      "val Loss: 0.5406 Acc: 0.7255\n",
      "has spend time 48m 0s/n\n",
      "\n",
      "Epoch 1348/9999\n",
      "----------\n",
      "train Loss: 0.5015 Acc: 0.7541\n",
      "has spend time 48m 2s/n\n",
      "val Loss: 0.5489 Acc: 0.7124\n",
      "has spend time 48m 2s/n\n",
      "\n",
      "Epoch 1349/9999\n",
      "----------\n",
      "train Loss: 0.4772 Acc: 0.7418\n",
      "has spend time 48m 4s/n\n",
      "val Loss: 0.5623 Acc: 0.6928\n",
      "has spend time 48m 4s/n\n",
      "\n",
      "Epoch 1350/9999\n",
      "----------\n",
      "train Loss: 0.4924 Acc: 0.7746\n",
      "has spend time 48m 6s/n\n",
      "val Loss: 0.5520 Acc: 0.7059\n",
      "has spend time 48m 7s/n\n",
      "\n",
      "Epoch 1351/9999\n",
      "----------\n",
      "train Loss: 0.5100 Acc: 0.7295\n",
      "has spend time 48m 8s/n\n",
      "val Loss: 0.5536 Acc: 0.7190\n",
      "has spend time 48m 9s/n\n",
      "\n",
      "Epoch 1352/9999\n",
      "----------\n",
      "train Loss: 0.5125 Acc: 0.7213\n",
      "has spend time 48m 10s/n\n",
      "val Loss: 0.5634 Acc: 0.6993\n",
      "has spend time 48m 11s/n\n",
      "\n",
      "Epoch 1353/9999\n",
      "----------\n",
      "train Loss: 0.5152 Acc: 0.7090\n",
      "has spend time 48m 12s/n\n",
      "val Loss: 0.5540 Acc: 0.7059\n",
      "has spend time 48m 13s/n\n",
      "\n",
      "Epoch 1354/9999\n",
      "----------\n",
      "train Loss: 0.5205 Acc: 0.7213\n",
      "has spend time 48m 14s/n\n",
      "val Loss: 0.5486 Acc: 0.6993\n",
      "has spend time 48m 15s/n\n",
      "\n",
      "Epoch 1355/9999\n",
      "----------\n",
      "train Loss: 0.5328 Acc: 0.7131\n",
      "has spend time 48m 16s/n\n",
      "val Loss: 0.5546 Acc: 0.6928\n",
      "has spend time 48m 17s/n\n",
      "\n",
      "Epoch 1356/9999\n",
      "----------\n",
      "train Loss: 0.4847 Acc: 0.7664\n",
      "has spend time 48m 18s/n\n",
      "val Loss: 0.5527 Acc: 0.6928\n",
      "has spend time 48m 19s/n\n",
      "\n",
      "Epoch 1357/9999\n",
      "----------\n",
      "train Loss: 0.4782 Acc: 0.7664\n",
      "has spend time 48m 21s/n\n",
      "val Loss: 0.5551 Acc: 0.6928\n",
      "has spend time 48m 21s/n\n",
      "\n",
      "Epoch 1358/9999\n",
      "----------\n",
      "train Loss: 0.5214 Acc: 0.7090\n",
      "has spend time 48m 23s/n\n",
      "val Loss: 0.5642 Acc: 0.6993\n",
      "has spend time 48m 24s/n\n",
      "\n",
      "Epoch 1359/9999\n",
      "----------\n",
      "train Loss: 0.5037 Acc: 0.7500\n",
      "has spend time 48m 25s/n\n",
      "val Loss: 0.5476 Acc: 0.7059\n",
      "has spend time 48m 26s/n\n",
      "\n",
      "Epoch 1360/9999\n",
      "----------\n",
      "train Loss: 0.5314 Acc: 0.7254\n",
      "has spend time 48m 27s/n\n",
      "val Loss: 0.5540 Acc: 0.7124\n",
      "has spend time 48m 28s/n\n",
      "\n",
      "Epoch 1361/9999\n",
      "----------\n",
      "train Loss: 0.5192 Acc: 0.7377\n",
      "has spend time 48m 29s/n\n",
      "val Loss: 0.5506 Acc: 0.7124\n",
      "has spend time 48m 30s/n\n",
      "\n",
      "Epoch 1362/9999\n",
      "----------\n",
      "train Loss: 0.5367 Acc: 0.7336\n",
      "has spend time 48m 31s/n\n",
      "val Loss: 0.5429 Acc: 0.7059\n",
      "has spend time 48m 32s/n\n",
      "\n",
      "Epoch 1363/9999\n",
      "----------\n",
      "train Loss: 0.5093 Acc: 0.7254\n",
      "has spend time 48m 33s/n\n",
      "val Loss: 0.5481 Acc: 0.7124\n",
      "has spend time 48m 34s/n\n",
      "\n",
      "Epoch 1364/9999\n",
      "----------\n",
      "train Loss: 0.5014 Acc: 0.7336\n",
      "has spend time 48m 35s/n\n",
      "val Loss: 0.5642 Acc: 0.6993\n",
      "has spend time 48m 36s/n\n",
      "\n",
      "Epoch 1365/9999\n",
      "----------\n",
      "train Loss: 0.4925 Acc: 0.7377\n",
      "has spend time 48m 37s/n\n",
      "val Loss: 0.5539 Acc: 0.7059\n",
      "has spend time 48m 38s/n\n",
      "\n",
      "Epoch 1366/9999\n",
      "----------\n",
      "train Loss: 0.5032 Acc: 0.7377\n",
      "has spend time 48m 39s/n\n",
      "val Loss: 0.5551 Acc: 0.7124\n",
      "has spend time 48m 40s/n\n",
      "\n",
      "Epoch 1367/9999\n",
      "----------\n",
      "train Loss: 0.4928 Acc: 0.7500\n",
      "has spend time 48m 41s/n\n",
      "val Loss: 0.5450 Acc: 0.7124\n",
      "has spend time 48m 42s/n\n",
      "\n",
      "Epoch 1368/9999\n",
      "----------\n",
      "train Loss: 0.4902 Acc: 0.7500\n",
      "has spend time 48m 44s/n\n",
      "val Loss: 0.5717 Acc: 0.6863\n",
      "has spend time 48m 44s/n\n",
      "\n",
      "Epoch 1369/9999\n",
      "----------\n",
      "train Loss: 0.5222 Acc: 0.7500\n",
      "has spend time 48m 46s/n\n",
      "val Loss: 0.5503 Acc: 0.7124\n",
      "has spend time 48m 46s/n\n",
      "\n",
      "Epoch 1370/9999\n",
      "----------\n",
      "train Loss: 0.5032 Acc: 0.7213\n",
      "has spend time 48m 48s/n\n",
      "val Loss: 0.5426 Acc: 0.7255\n",
      "has spend time 48m 48s/n\n",
      "\n",
      "Epoch 1371/9999\n",
      "----------\n",
      "train Loss: 0.5153 Acc: 0.7008\n",
      "has spend time 48m 50s/n\n",
      "val Loss: 0.5459 Acc: 0.7124\n",
      "has spend time 48m 51s/n\n",
      "\n",
      "Epoch 1372/9999\n",
      "----------\n",
      "train Loss: 0.5163 Acc: 0.7131\n",
      "has spend time 48m 52s/n\n",
      "val Loss: 0.5478 Acc: 0.7190\n",
      "has spend time 48m 53s/n\n",
      "\n",
      "Epoch 1373/9999\n",
      "----------\n",
      "train Loss: 0.4890 Acc: 0.7459\n",
      "has spend time 48m 55s/n\n",
      "val Loss: 0.5422 Acc: 0.7190\n",
      "has spend time 48m 55s/n\n",
      "\n",
      "Epoch 1374/9999\n",
      "----------\n",
      "train Loss: 0.5020 Acc: 0.7336\n",
      "has spend time 48m 57s/n\n",
      "val Loss: 0.5556 Acc: 0.6993\n",
      "has spend time 48m 57s/n\n",
      "\n",
      "Epoch 1375/9999\n",
      "----------\n",
      "train Loss: 0.5152 Acc: 0.7295\n",
      "has spend time 48m 59s/n\n",
      "val Loss: 0.5379 Acc: 0.7255\n",
      "has spend time 48m 59s/n\n",
      "\n",
      "Epoch 1376/9999\n",
      "----------\n",
      "train Loss: 0.5174 Acc: 0.7254\n",
      "has spend time 49m 1s/n\n",
      "val Loss: 0.5479 Acc: 0.6993\n",
      "has spend time 49m 2s/n\n",
      "\n",
      "Epoch 1377/9999\n",
      "----------\n",
      "train Loss: 0.5967 Acc: 0.6721\n",
      "has spend time 49m 3s/n\n",
      "val Loss: 0.5426 Acc: 0.7124\n",
      "has spend time 49m 4s/n\n",
      "\n",
      "Epoch 1378/9999\n",
      "----------\n",
      "train Loss: 0.5199 Acc: 0.7008\n",
      "has spend time 49m 5s/n\n",
      "val Loss: 0.5567 Acc: 0.6863\n",
      "has spend time 49m 6s/n\n",
      "\n",
      "Epoch 1379/9999\n",
      "----------\n",
      "train Loss: 0.5111 Acc: 0.7213\n",
      "has spend time 49m 7s/n\n",
      "val Loss: 0.5405 Acc: 0.7124\n",
      "has spend time 49m 8s/n\n",
      "\n",
      "Epoch 1380/9999\n",
      "----------\n",
      "train Loss: 0.5094 Acc: 0.7418\n",
      "has spend time 49m 10s/n\n",
      "val Loss: 0.5452 Acc: 0.7059\n",
      "has spend time 49m 11s/n\n",
      "\n",
      "Epoch 1381/9999\n",
      "----------\n",
      "train Loss: 0.5051 Acc: 0.7131\n",
      "has spend time 49m 12s/n\n",
      "val Loss: 0.5573 Acc: 0.6928\n",
      "has spend time 49m 13s/n\n",
      "\n",
      "Epoch 1382/9999\n",
      "----------\n",
      "train Loss: 0.5333 Acc: 0.7213\n",
      "has spend time 49m 14s/n\n",
      "val Loss: 0.5695 Acc: 0.6928\n",
      "has spend time 49m 15s/n\n",
      "\n",
      "Epoch 1383/9999\n",
      "----------\n",
      "train Loss: 0.5317 Acc: 0.7008\n",
      "has spend time 49m 16s/n\n",
      "val Loss: 0.5442 Acc: 0.7255\n",
      "has spend time 49m 17s/n\n",
      "\n",
      "Epoch 1384/9999\n",
      "----------\n",
      "train Loss: 0.5347 Acc: 0.6844\n",
      "has spend time 49m 18s/n\n",
      "val Loss: 0.5477 Acc: 0.7124\n",
      "has spend time 49m 19s/n\n",
      "\n",
      "Epoch 1385/9999\n",
      "----------\n",
      "train Loss: 0.5112 Acc: 0.7090\n",
      "has spend time 49m 20s/n\n",
      "val Loss: 0.5552 Acc: 0.6928\n",
      "has spend time 49m 21s/n\n",
      "\n",
      "Epoch 1386/9999\n",
      "----------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train Loss: 0.4882 Acc: 0.7582\n",
      "has spend time 49m 23s/n\n",
      "val Loss: 0.5465 Acc: 0.7124\n",
      "has spend time 49m 23s/n\n",
      "\n",
      "Epoch 1387/9999\n",
      "----------\n",
      "train Loss: 0.5119 Acc: 0.7500\n",
      "has spend time 49m 25s/n\n",
      "val Loss: 0.5579 Acc: 0.6993\n",
      "has spend time 49m 26s/n\n",
      "\n",
      "Epoch 1388/9999\n",
      "----------\n",
      "train Loss: 0.5140 Acc: 0.7172\n",
      "has spend time 49m 27s/n\n",
      "val Loss: 0.5559 Acc: 0.6993\n",
      "has spend time 49m 28s/n\n",
      "\n",
      "Epoch 1389/9999\n",
      "----------\n",
      "train Loss: 0.5159 Acc: 0.7705\n",
      "has spend time 49m 29s/n\n",
      "val Loss: 0.5508 Acc: 0.7190\n",
      "has spend time 49m 30s/n\n",
      "\n",
      "Epoch 1390/9999\n",
      "----------\n",
      "train Loss: 0.5083 Acc: 0.7295\n",
      "has spend time 49m 31s/n\n",
      "val Loss: 0.5556 Acc: 0.7124\n",
      "has spend time 49m 32s/n\n",
      "\n",
      "Epoch 1391/9999\n",
      "----------\n",
      "train Loss: 0.4822 Acc: 0.7828\n",
      "has spend time 49m 33s/n\n",
      "val Loss: 0.5520 Acc: 0.7059\n",
      "has spend time 49m 34s/n\n",
      "\n",
      "Epoch 1392/9999\n",
      "----------\n",
      "train Loss: 0.4731 Acc: 0.7500\n",
      "has spend time 49m 36s/n\n",
      "val Loss: 0.5540 Acc: 0.7124\n",
      "has spend time 49m 37s/n\n",
      "\n",
      "Epoch 1393/9999\n",
      "----------\n",
      "train Loss: 0.4839 Acc: 0.7746\n",
      "has spend time 49m 38s/n\n",
      "val Loss: 0.5547 Acc: 0.6993\n",
      "has spend time 49m 39s/n\n",
      "\n",
      "Epoch 1394/9999\n",
      "----------\n",
      "train Loss: 0.4806 Acc: 0.7705\n",
      "has spend time 49m 40s/n\n",
      "val Loss: 0.5452 Acc: 0.7059\n",
      "has spend time 49m 41s/n\n",
      "\n",
      "Epoch 1395/9999\n",
      "----------\n",
      "train Loss: 0.4989 Acc: 0.7418\n",
      "has spend time 49m 42s/n\n",
      "val Loss: 0.5598 Acc: 0.7059\n",
      "has spend time 49m 43s/n\n",
      "\n",
      "Epoch 1396/9999\n",
      "----------\n",
      "train Loss: 0.4891 Acc: 0.7705\n",
      "has spend time 49m 45s/n\n",
      "val Loss: 0.5473 Acc: 0.7059\n",
      "has spend time 49m 45s/n\n",
      "\n",
      "Epoch 1397/9999\n",
      "----------\n",
      "train Loss: 0.4967 Acc: 0.7254\n",
      "has spend time 49m 47s/n\n",
      "val Loss: 0.5662 Acc: 0.7124\n",
      "has spend time 49m 47s/n\n",
      "\n",
      "Epoch 1398/9999\n",
      "----------\n",
      "train Loss: 0.5038 Acc: 0.7377\n",
      "has spend time 49m 49s/n\n",
      "val Loss: 0.5583 Acc: 0.6993\n",
      "has spend time 49m 50s/n\n",
      "\n",
      "Epoch 1399/9999\n",
      "----------\n",
      "train Loss: 0.4948 Acc: 0.7459\n",
      "has spend time 49m 51s/n\n",
      "val Loss: 0.5446 Acc: 0.7124\n",
      "has spend time 49m 52s/n\n",
      "\n",
      "Epoch 1400/9999\n",
      "----------\n",
      "train Loss: 0.5115 Acc: 0.7254\n",
      "has spend time 49m 53s/n\n",
      "val Loss: 0.5676 Acc: 0.6993\n",
      "has spend time 49m 54s/n\n",
      "\n",
      "Epoch 1401/9999\n",
      "----------\n",
      "train Loss: 0.5165 Acc: 0.7459\n",
      "has spend time 49m 55s/n\n",
      "val Loss: 0.5553 Acc: 0.7059\n",
      "has spend time 49m 56s/n\n",
      "\n",
      "Epoch 1402/9999\n",
      "----------\n",
      "train Loss: 0.5040 Acc: 0.7336\n",
      "has spend time 49m 57s/n\n",
      "val Loss: 0.5456 Acc: 0.7059\n",
      "has spend time 49m 58s/n\n",
      "\n",
      "Epoch 1403/9999\n",
      "----------\n",
      "train Loss: 0.4969 Acc: 0.7254\n",
      "has spend time 49m 59s/n\n",
      "val Loss: 0.5468 Acc: 0.7190\n",
      "has spend time 49m 60s/n\n",
      "\n",
      "Epoch 1404/9999\n",
      "----------\n",
      "train Loss: 0.5048 Acc: 0.7295\n",
      "has spend time 50m 1s/n\n",
      "val Loss: 0.5501 Acc: 0.7124\n",
      "has spend time 50m 2s/n\n",
      "\n",
      "Epoch 1405/9999\n",
      "----------\n",
      "train Loss: 0.4928 Acc: 0.7377\n",
      "has spend time 50m 3s/n\n",
      "val Loss: 0.5524 Acc: 0.7059\n",
      "has spend time 50m 4s/n\n",
      "\n",
      "Epoch 1406/9999\n",
      "----------\n",
      "train Loss: 0.4959 Acc: 0.7541\n",
      "has spend time 50m 5s/n\n",
      "val Loss: 0.5687 Acc: 0.6928\n",
      "has spend time 50m 6s/n\n",
      "\n",
      "Epoch 1407/9999\n",
      "----------\n",
      "train Loss: 0.4852 Acc: 0.7705\n",
      "has spend time 50m 7s/n\n",
      "val Loss: 0.5489 Acc: 0.7124\n",
      "has spend time 50m 8s/n\n",
      "\n",
      "Epoch 1408/9999\n",
      "----------\n",
      "train Loss: 0.4952 Acc: 0.7705\n",
      "has spend time 50m 10s/n\n",
      "val Loss: 0.5518 Acc: 0.7124\n",
      "has spend time 50m 10s/n\n",
      "\n",
      "Epoch 1409/9999\n",
      "----------\n",
      "train Loss: 0.5123 Acc: 0.7377\n",
      "has spend time 50m 12s/n\n",
      "val Loss: 0.5509 Acc: 0.7124\n",
      "has spend time 50m 13s/n\n",
      "\n",
      "Epoch 1410/9999\n",
      "----------\n",
      "train Loss: 0.5208 Acc: 0.7418\n",
      "has spend time 50m 14s/n\n",
      "val Loss: 0.5465 Acc: 0.7124\n",
      "has spend time 50m 15s/n\n",
      "\n",
      "Epoch 1411/9999\n",
      "----------\n",
      "train Loss: 0.5035 Acc: 0.7664\n",
      "has spend time 50m 16s/n\n",
      "val Loss: 0.5471 Acc: 0.7124\n",
      "has spend time 50m 17s/n\n",
      "\n",
      "Epoch 1412/9999\n",
      "----------\n",
      "train Loss: 0.5036 Acc: 0.7254\n",
      "has spend time 50m 18s/n\n",
      "val Loss: 0.5540 Acc: 0.7124\n",
      "has spend time 50m 19s/n\n",
      "\n",
      "Epoch 1413/9999\n",
      "----------\n",
      "train Loss: 0.5168 Acc: 0.6967\n",
      "has spend time 50m 20s/n\n",
      "val Loss: 0.5469 Acc: 0.7059\n",
      "has spend time 50m 21s/n\n",
      "\n",
      "Epoch 1414/9999\n",
      "----------\n",
      "train Loss: 0.5221 Acc: 0.7541\n",
      "has spend time 50m 23s/n\n",
      "val Loss: 0.5483 Acc: 0.7190\n",
      "has spend time 50m 23s/n\n",
      "\n",
      "Epoch 1415/9999\n",
      "----------\n",
      "train Loss: 0.4902 Acc: 0.7787\n",
      "has spend time 50m 25s/n\n",
      "val Loss: 0.5484 Acc: 0.6993\n",
      "has spend time 50m 25s/n\n",
      "\n",
      "Epoch 1416/9999\n",
      "----------\n",
      "train Loss: 0.5116 Acc: 0.7418\n",
      "has spend time 50m 27s/n\n",
      "val Loss: 0.5526 Acc: 0.7059\n",
      "has spend time 50m 27s/n\n",
      "\n",
      "Epoch 1417/9999\n",
      "----------\n",
      "train Loss: 0.5119 Acc: 0.7418\n",
      "has spend time 50m 29s/n\n",
      "val Loss: 0.5420 Acc: 0.7190\n",
      "has spend time 50m 29s/n\n",
      "\n",
      "Epoch 1418/9999\n",
      "----------\n",
      "train Loss: 0.5425 Acc: 0.6967\n",
      "has spend time 50m 31s/n\n",
      "val Loss: 0.5469 Acc: 0.7059\n",
      "has spend time 50m 31s/n\n",
      "\n",
      "Epoch 1419/9999\n",
      "----------\n",
      "train Loss: 0.5117 Acc: 0.7131\n",
      "has spend time 50m 33s/n\n",
      "val Loss: 0.5557 Acc: 0.7124\n",
      "has spend time 50m 34s/n\n",
      "\n",
      "Epoch 1420/9999\n",
      "----------\n",
      "train Loss: 0.5054 Acc: 0.7172\n",
      "has spend time 50m 35s/n\n",
      "val Loss: 0.5410 Acc: 0.7124\n",
      "has spend time 50m 36s/n\n",
      "\n",
      "Epoch 1421/9999\n",
      "----------\n",
      "train Loss: 0.4936 Acc: 0.7418\n",
      "has spend time 50m 37s/n\n",
      "val Loss: 0.5533 Acc: 0.6928\n",
      "has spend time 50m 38s/n\n",
      "\n",
      "Epoch 1422/9999\n",
      "----------\n",
      "train Loss: 0.4877 Acc: 0.7500\n",
      "has spend time 50m 39s/n\n",
      "val Loss: 0.5518 Acc: 0.7124\n",
      "has spend time 50m 40s/n\n",
      "\n",
      "Epoch 1423/9999\n",
      "----------\n",
      "train Loss: 0.5241 Acc: 0.7336\n",
      "has spend time 50m 41s/n\n",
      "val Loss: 0.5474 Acc: 0.7124\n",
      "has spend time 50m 42s/n\n",
      "\n",
      "Epoch 1424/9999\n",
      "----------\n",
      "train Loss: 0.5094 Acc: 0.7377\n",
      "has spend time 50m 43s/n\n",
      "val Loss: 0.5493 Acc: 0.7190\n",
      "has spend time 50m 44s/n\n",
      "\n",
      "Epoch 1425/9999\n",
      "----------\n",
      "train Loss: 0.4918 Acc: 0.7377\n",
      "has spend time 50m 45s/n\n",
      "val Loss: 0.5544 Acc: 0.7059\n",
      "has spend time 50m 46s/n\n",
      "\n",
      "Epoch 1426/9999\n",
      "----------\n",
      "train Loss: 0.4942 Acc: 0.7582\n",
      "has spend time 50m 48s/n\n",
      "val Loss: 0.5408 Acc: 0.7124\n",
      "has spend time 50m 49s/n\n",
      "\n",
      "Epoch 1427/9999\n",
      "----------\n",
      "train Loss: 0.5113 Acc: 0.7500\n",
      "has spend time 50m 50s/n\n",
      "val Loss: 0.5499 Acc: 0.7124\n",
      "has spend time 50m 51s/n\n",
      "\n",
      "Epoch 1428/9999\n",
      "----------\n",
      "train Loss: 0.4964 Acc: 0.7336\n",
      "has spend time 50m 52s/n\n",
      "val Loss: 0.5519 Acc: 0.6993\n",
      "has spend time 50m 53s/n\n",
      "\n",
      "Epoch 1429/9999\n",
      "----------\n",
      "train Loss: 0.4934 Acc: 0.7664\n",
      "has spend time 50m 54s/n\n",
      "val Loss: 0.5537 Acc: 0.7124\n",
      "has spend time 50m 55s/n\n",
      "\n",
      "Epoch 1430/9999\n",
      "----------\n",
      "train Loss: 0.5062 Acc: 0.7295\n",
      "has spend time 50m 56s/n\n",
      "val Loss: 0.5450 Acc: 0.7190\n",
      "has spend time 50m 57s/n\n",
      "\n",
      "Epoch 1431/9999\n",
      "----------\n",
      "train Loss: 0.4915 Acc: 0.7705\n",
      "has spend time 50m 58s/n\n",
      "val Loss: 0.5455 Acc: 0.7190\n",
      "has spend time 50m 59s/n\n",
      "\n",
      "Epoch 1432/9999\n",
      "----------\n",
      "train Loss: 0.4617 Acc: 0.7705\n",
      "has spend time 51m 0s/n\n",
      "val Loss: 0.5492 Acc: 0.7124\n",
      "has spend time 51m 1s/n\n",
      "\n",
      "Epoch 1433/9999\n",
      "----------\n",
      "train Loss: 0.5085 Acc: 0.7336\n",
      "has spend time 51m 2s/n\n",
      "val Loss: 0.5441 Acc: 0.7190\n",
      "has spend time 51m 3s/n\n",
      "\n",
      "Epoch 1434/9999\n",
      "----------\n",
      "train Loss: 0.4984 Acc: 0.7500\n",
      "has spend time 51m 5s/n\n",
      "val Loss: 0.5502 Acc: 0.7255\n",
      "has spend time 51m 5s/n\n",
      "\n",
      "Epoch 1435/9999\n",
      "----------\n",
      "train Loss: 0.4973 Acc: 0.7828\n",
      "has spend time 51m 7s/n\n",
      "val Loss: 0.5553 Acc: 0.6993\n",
      "has spend time 51m 7s/n\n",
      "\n",
      "Epoch 1436/9999\n",
      "----------\n",
      "train Loss: 0.4999 Acc: 0.7336\n",
      "has spend time 51m 9s/n\n",
      "val Loss: 0.5511 Acc: 0.6993\n",
      "has spend time 51m 9s/n\n",
      "\n",
      "Epoch 1437/9999\n",
      "----------\n",
      "train Loss: 0.4943 Acc: 0.7459\n",
      "has spend time 51m 11s/n\n",
      "val Loss: 0.5530 Acc: 0.7059\n",
      "has spend time 51m 11s/n\n",
      "\n",
      "Epoch 1438/9999\n",
      "----------\n",
      "train Loss: 0.4845 Acc: 0.7623\n",
      "has spend time 51m 13s/n\n",
      "val Loss: 0.5491 Acc: 0.7059\n",
      "has spend time 51m 13s/n\n",
      "\n",
      "Epoch 1439/9999\n",
      "----------\n",
      "train Loss: 0.4778 Acc: 0.7746\n",
      "has spend time 51m 15s/n\n",
      "val Loss: 0.5504 Acc: 0.7059\n",
      "has spend time 51m 16s/n\n",
      "\n",
      "Epoch 1440/9999\n",
      "----------\n",
      "train Loss: 0.5166 Acc: 0.7254\n",
      "has spend time 51m 17s/n\n",
      "val Loss: 0.5722 Acc: 0.6993\n",
      "has spend time 51m 18s/n\n",
      "\n",
      "Epoch 1441/9999\n",
      "----------\n",
      "train Loss: 0.4959 Acc: 0.7336\n",
      "has spend time 51m 19s/n\n",
      "val Loss: 0.5815 Acc: 0.6993\n",
      "has spend time 51m 20s/n\n",
      "\n",
      "Epoch 1442/9999\n",
      "----------\n",
      "train Loss: 0.5172 Acc: 0.7172\n",
      "has spend time 51m 21s/n\n",
      "val Loss: 0.5767 Acc: 0.7059\n",
      "has spend time 51m 22s/n\n",
      "\n",
      "Epoch 1443/9999\n",
      "----------\n",
      "train Loss: 0.5210 Acc: 0.7172\n",
      "has spend time 51m 24s/n\n",
      "val Loss: 0.5585 Acc: 0.6993\n",
      "has spend time 51m 25s/n\n",
      "\n",
      "Epoch 1444/9999\n",
      "----------\n",
      "train Loss: 0.5425 Acc: 0.6803\n",
      "has spend time 51m 26s/n\n",
      "val Loss: 0.5485 Acc: 0.7190\n",
      "has spend time 51m 27s/n\n",
      "\n",
      "Epoch 1445/9999\n",
      "----------\n",
      "train Loss: 0.5131 Acc: 0.7500\n",
      "has spend time 51m 28s/n\n",
      "val Loss: 0.5524 Acc: 0.7124\n",
      "has spend time 51m 29s/n\n",
      "\n",
      "Epoch 1446/9999\n",
      "----------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train Loss: 0.5074 Acc: 0.7172\n",
      "has spend time 51m 31s/n\n",
      "val Loss: 0.5544 Acc: 0.7059\n",
      "has spend time 51m 31s/n\n",
      "\n",
      "Epoch 1447/9999\n",
      "----------\n",
      "train Loss: 0.4930 Acc: 0.7828\n",
      "has spend time 51m 33s/n\n",
      "val Loss: 0.5538 Acc: 0.7124\n",
      "has spend time 51m 33s/n\n",
      "\n",
      "Epoch 1448/9999\n",
      "----------\n",
      "train Loss: 0.5190 Acc: 0.7254\n",
      "has spend time 51m 35s/n\n",
      "val Loss: 0.5601 Acc: 0.6928\n",
      "has spend time 51m 35s/n\n",
      "\n",
      "Epoch 1449/9999\n",
      "----------\n",
      "train Loss: 0.5109 Acc: 0.7295\n",
      "has spend time 51m 37s/n\n",
      "val Loss: 0.5667 Acc: 0.6993\n",
      "has spend time 51m 37s/n\n",
      "\n",
      "Epoch 1450/9999\n",
      "----------\n",
      "train Loss: 0.5122 Acc: 0.7418\n",
      "has spend time 51m 39s/n\n",
      "val Loss: 0.5614 Acc: 0.6928\n",
      "has spend time 51m 39s/n\n",
      "\n",
      "Epoch 1451/9999\n",
      "----------\n",
      "train Loss: 0.4913 Acc: 0.7623\n",
      "has spend time 51m 41s/n\n",
      "val Loss: 0.5597 Acc: 0.6993\n",
      "has spend time 51m 42s/n\n",
      "\n",
      "Epoch 1452/9999\n",
      "----------\n",
      "train Loss: 0.5053 Acc: 0.7172\n",
      "has spend time 51m 43s/n\n",
      "val Loss: 0.5395 Acc: 0.7190\n",
      "has spend time 51m 44s/n\n",
      "\n",
      "Epoch 1453/9999\n",
      "----------\n",
      "train Loss: 0.5111 Acc: 0.7049\n",
      "has spend time 51m 45s/n\n",
      "val Loss: 0.5450 Acc: 0.7124\n",
      "has spend time 51m 46s/n\n",
      "\n",
      "Epoch 1454/9999\n",
      "----------\n",
      "train Loss: 0.5110 Acc: 0.7418\n",
      "has spend time 51m 47s/n\n",
      "val Loss: 0.5610 Acc: 0.6993\n",
      "has spend time 51m 48s/n\n",
      "\n",
      "Epoch 1455/9999\n",
      "----------\n",
      "train Loss: 0.4915 Acc: 0.7582\n",
      "has spend time 51m 49s/n\n",
      "val Loss: 0.5485 Acc: 0.7124\n",
      "has spend time 51m 50s/n\n",
      "\n",
      "Epoch 1456/9999\n",
      "----------\n",
      "train Loss: 0.4801 Acc: 0.7910\n",
      "has spend time 51m 51s/n\n",
      "val Loss: 0.5476 Acc: 0.7190\n",
      "has spend time 51m 52s/n\n",
      "\n",
      "Epoch 1457/9999\n",
      "----------\n",
      "train Loss: 0.5105 Acc: 0.7295\n",
      "has spend time 51m 53s/n\n",
      "val Loss: 0.5543 Acc: 0.7124\n",
      "has spend time 51m 54s/n\n",
      "\n",
      "Epoch 1458/9999\n",
      "----------\n",
      "train Loss: 0.5009 Acc: 0.7459\n",
      "has spend time 51m 55s/n\n",
      "val Loss: 0.5632 Acc: 0.7059\n",
      "has spend time 51m 56s/n\n",
      "\n",
      "Epoch 1459/9999\n",
      "----------\n",
      "train Loss: 0.4952 Acc: 0.7500\n",
      "has spend time 51m 57s/n\n",
      "val Loss: 0.5552 Acc: 0.6993\n",
      "has spend time 51m 58s/n\n",
      "\n",
      "Epoch 1460/9999\n",
      "----------\n",
      "train Loss: 0.5002 Acc: 0.7541\n",
      "has spend time 51m 60s/n\n",
      "val Loss: 0.5709 Acc: 0.6993\n",
      "has spend time 52m 1s/n\n",
      "\n",
      "Epoch 1461/9999\n",
      "----------\n",
      "train Loss: 0.4796 Acc: 0.7623\n",
      "has spend time 52m 2s/n\n",
      "val Loss: 0.5477 Acc: 0.7124\n",
      "has spend time 52m 3s/n\n",
      "\n",
      "Epoch 1462/9999\n",
      "----------\n",
      "train Loss: 0.5150 Acc: 0.7131\n",
      "has spend time 52m 4s/n\n",
      "val Loss: 0.5639 Acc: 0.6928\n",
      "has spend time 52m 5s/n\n",
      "\n",
      "Epoch 1463/9999\n",
      "----------\n",
      "train Loss: 0.4991 Acc: 0.7090\n",
      "has spend time 52m 6s/n\n",
      "val Loss: 0.5582 Acc: 0.6928\n",
      "has spend time 52m 7s/n\n",
      "\n",
      "Epoch 1464/9999\n",
      "----------\n",
      "train Loss: 0.5302 Acc: 0.7172\n",
      "has spend time 52m 8s/n\n",
      "val Loss: 0.5551 Acc: 0.7059\n",
      "has spend time 52m 9s/n\n",
      "\n",
      "Epoch 1465/9999\n",
      "----------\n",
      "train Loss: 0.5116 Acc: 0.7582\n",
      "has spend time 52m 10s/n\n",
      "val Loss: 0.5401 Acc: 0.7124\n",
      "has spend time 52m 11s/n\n",
      "\n",
      "Epoch 1466/9999\n",
      "----------\n",
      "train Loss: 0.4961 Acc: 0.7623\n",
      "has spend time 52m 13s/n\n",
      "val Loss: 0.5531 Acc: 0.7059\n",
      "has spend time 52m 13s/n\n",
      "\n",
      "Epoch 1467/9999\n",
      "----------\n",
      "train Loss: 0.5273 Acc: 0.7623\n",
      "has spend time 52m 15s/n\n",
      "val Loss: 0.5560 Acc: 0.6928\n",
      "has spend time 52m 15s/n\n",
      "\n",
      "Epoch 1468/9999\n",
      "----------\n",
      "train Loss: 0.5333 Acc: 0.7172\n",
      "has spend time 52m 17s/n\n",
      "val Loss: 0.5601 Acc: 0.7059\n",
      "has spend time 52m 17s/n\n",
      "\n",
      "Epoch 1469/9999\n",
      "----------\n",
      "train Loss: 0.4727 Acc: 0.7787\n",
      "has spend time 52m 19s/n\n",
      "val Loss: 0.5641 Acc: 0.6993\n",
      "has spend time 52m 19s/n\n",
      "\n",
      "Epoch 1470/9999\n",
      "----------\n",
      "train Loss: 0.4993 Acc: 0.7336\n",
      "has spend time 52m 21s/n\n",
      "val Loss: 0.5593 Acc: 0.6993\n",
      "has spend time 52m 22s/n\n",
      "\n",
      "Epoch 1471/9999\n",
      "----------\n",
      "train Loss: 0.5054 Acc: 0.7459\n",
      "has spend time 52m 23s/n\n",
      "val Loss: 0.5489 Acc: 0.7124\n",
      "has spend time 52m 24s/n\n",
      "\n",
      "Epoch 1472/9999\n",
      "----------\n",
      "train Loss: 0.4965 Acc: 0.7213\n",
      "has spend time 52m 25s/n\n",
      "val Loss: 0.5443 Acc: 0.7059\n",
      "has spend time 52m 26s/n\n",
      "\n",
      "Epoch 1473/9999\n",
      "----------\n",
      "train Loss: 0.5068 Acc: 0.7541\n",
      "has spend time 52m 28s/n\n",
      "val Loss: 0.5616 Acc: 0.6993\n",
      "has spend time 52m 28s/n\n",
      "\n",
      "Epoch 1474/9999\n",
      "----------\n",
      "train Loss: 0.5254 Acc: 0.6926\n",
      "has spend time 52m 30s/n\n",
      "val Loss: 0.5593 Acc: 0.6993\n",
      "has spend time 52m 30s/n\n",
      "\n",
      "Epoch 1475/9999\n",
      "----------\n",
      "train Loss: 0.5184 Acc: 0.7459\n",
      "has spend time 52m 32s/n\n",
      "val Loss: 0.5602 Acc: 0.6993\n",
      "has spend time 52m 32s/n\n",
      "\n",
      "Epoch 1476/9999\n",
      "----------\n",
      "train Loss: 0.4988 Acc: 0.7459\n",
      "has spend time 52m 34s/n\n",
      "val Loss: 0.5511 Acc: 0.7059\n",
      "has spend time 52m 34s/n\n",
      "\n",
      "Epoch 1477/9999\n",
      "----------\n",
      "train Loss: 0.5099 Acc: 0.7131\n",
      "has spend time 52m 36s/n\n",
      "val Loss: 0.5541 Acc: 0.7059\n",
      "has spend time 52m 37s/n\n",
      "\n",
      "Epoch 1478/9999\n",
      "----------\n",
      "train Loss: 0.5232 Acc: 0.7582\n",
      "has spend time 52m 38s/n\n",
      "val Loss: 0.5558 Acc: 0.6928\n",
      "has spend time 52m 39s/n\n",
      "\n",
      "Epoch 1479/9999\n",
      "----------\n",
      "train Loss: 0.5126 Acc: 0.7336\n",
      "has spend time 52m 40s/n\n",
      "val Loss: 0.5466 Acc: 0.7255\n",
      "has spend time 52m 41s/n\n",
      "\n",
      "Epoch 1480/9999\n",
      "----------\n",
      "train Loss: 0.5078 Acc: 0.7254\n",
      "has spend time 52m 42s/n\n",
      "val Loss: 0.5531 Acc: 0.7124\n",
      "has spend time 52m 43s/n\n",
      "\n",
      "Epoch 1481/9999\n",
      "----------\n",
      "train Loss: 0.4644 Acc: 0.7705\n",
      "has spend time 52m 44s/n\n",
      "val Loss: 0.5449 Acc: 0.7124\n",
      "has spend time 52m 45s/n\n",
      "\n",
      "Epoch 1482/9999\n",
      "----------\n",
      "train Loss: 0.5001 Acc: 0.7500\n",
      "has spend time 52m 46s/n\n",
      "val Loss: 0.5510 Acc: 0.7059\n",
      "has spend time 52m 47s/n\n",
      "\n",
      "Epoch 1483/9999\n",
      "----------\n",
      "train Loss: 0.5160 Acc: 0.7787\n",
      "has spend time 52m 48s/n\n",
      "val Loss: 0.5603 Acc: 0.6993\n",
      "has spend time 52m 49s/n\n",
      "\n",
      "Epoch 1484/9999\n",
      "----------\n",
      "train Loss: 0.5109 Acc: 0.7295\n",
      "has spend time 52m 50s/n\n",
      "val Loss: 0.5511 Acc: 0.7059\n",
      "has spend time 52m 51s/n\n",
      "\n",
      "Epoch 1485/9999\n",
      "----------\n",
      "train Loss: 0.5257 Acc: 0.7377\n",
      "has spend time 52m 53s/n\n",
      "val Loss: 0.5401 Acc: 0.7190\n",
      "has spend time 52m 53s/n\n",
      "\n",
      "Epoch 1486/9999\n",
      "----------\n",
      "train Loss: 0.5308 Acc: 0.7295\n",
      "has spend time 52m 55s/n\n",
      "val Loss: 0.5478 Acc: 0.7059\n",
      "has spend time 52m 56s/n\n",
      "\n",
      "Epoch 1487/9999\n",
      "----------\n",
      "train Loss: 0.5202 Acc: 0.7623\n",
      "has spend time 52m 57s/n\n",
      "val Loss: 0.5537 Acc: 0.6993\n",
      "has spend time 52m 58s/n\n",
      "\n",
      "Epoch 1488/9999\n",
      "----------\n",
      "train Loss: 0.5209 Acc: 0.7418\n",
      "has spend time 52m 59s/n\n",
      "val Loss: 0.5572 Acc: 0.7059\n",
      "has spend time 52m 60s/n\n",
      "\n",
      "Epoch 1489/9999\n",
      "----------\n",
      "train Loss: 0.4750 Acc: 0.7664\n",
      "has spend time 53m 1s/n\n",
      "val Loss: 0.5471 Acc: 0.7124\n",
      "has spend time 53m 2s/n\n",
      "\n",
      "Epoch 1490/9999\n",
      "----------\n",
      "train Loss: 0.5026 Acc: 0.7131\n",
      "has spend time 53m 3s/n\n",
      "val Loss: 0.5544 Acc: 0.6928\n",
      "has spend time 53m 4s/n\n",
      "\n",
      "Epoch 1491/9999\n",
      "----------\n",
      "train Loss: 0.4938 Acc: 0.7295\n",
      "has spend time 53m 6s/n\n",
      "val Loss: 0.5420 Acc: 0.7190\n",
      "has spend time 53m 6s/n\n",
      "\n",
      "Epoch 1492/9999\n",
      "----------\n",
      "train Loss: 0.5249 Acc: 0.7336\n",
      "has spend time 53m 8s/n\n",
      "val Loss: 0.5454 Acc: 0.7255\n",
      "has spend time 53m 8s/n\n",
      "\n",
      "Epoch 1493/9999\n",
      "----------\n",
      "train Loss: 0.5262 Acc: 0.7500\n",
      "has spend time 53m 10s/n\n",
      "val Loss: 0.5600 Acc: 0.7059\n",
      "has spend time 53m 10s/n\n",
      "\n",
      "Epoch 1494/9999\n",
      "----------\n",
      "train Loss: 0.5272 Acc: 0.7377\n",
      "has spend time 53m 12s/n\n",
      "val Loss: 0.5567 Acc: 0.7059\n",
      "has spend time 53m 12s/n\n",
      "\n",
      "Epoch 1495/9999\n",
      "----------\n",
      "train Loss: 0.5068 Acc: 0.7336\n",
      "has spend time 53m 14s/n\n",
      "val Loss: 0.5533 Acc: 0.7059\n",
      "has spend time 53m 14s/n\n",
      "\n",
      "Epoch 1496/9999\n",
      "----------\n",
      "train Loss: 0.4957 Acc: 0.7541\n",
      "has spend time 53m 16s/n\n",
      "val Loss: 0.5539 Acc: 0.6928\n",
      "has spend time 53m 16s/n\n",
      "\n",
      "Epoch 1497/9999\n",
      "----------\n",
      "train Loss: 0.4876 Acc: 0.7705\n",
      "has spend time 53m 18s/n\n",
      "val Loss: 0.5644 Acc: 0.6993\n",
      "has spend time 53m 19s/n\n",
      "\n",
      "Epoch 1498/9999\n",
      "----------\n",
      "train Loss: 0.5239 Acc: 0.7459\n",
      "has spend time 53m 20s/n\n",
      "val Loss: 0.5439 Acc: 0.7255\n",
      "has spend time 53m 21s/n\n",
      "\n",
      "Epoch 1499/9999\n",
      "----------\n",
      "train Loss: 0.4617 Acc: 0.7664\n",
      "has spend time 53m 22s/n\n",
      "val Loss: 0.5527 Acc: 0.7190\n",
      "has spend time 53m 23s/n\n",
      "\n",
      "Epoch 1500/9999\n",
      "----------\n",
      "train Loss: 0.4888 Acc: 0.7582\n",
      "has spend time 53m 25s/n\n",
      "val Loss: 0.5428 Acc: 0.7255\n",
      "has spend time 53m 25s/n\n",
      "\n",
      "Epoch 1501/9999\n",
      "----------\n",
      "train Loss: 0.4813 Acc: 0.7459\n",
      "has spend time 53m 27s/n\n",
      "val Loss: 0.5473 Acc: 0.7124\n",
      "has spend time 53m 28s/n\n",
      "\n",
      "Epoch 1502/9999\n",
      "----------\n",
      "train Loss: 0.5036 Acc: 0.7418\n",
      "has spend time 53m 29s/n\n",
      "val Loss: 0.5450 Acc: 0.7124\n",
      "has spend time 53m 30s/n\n",
      "\n",
      "Epoch 1503/9999\n",
      "----------\n",
      "train Loss: 0.4945 Acc: 0.7418\n",
      "has spend time 53m 31s/n\n",
      "val Loss: 0.5509 Acc: 0.6993\n",
      "has spend time 53m 32s/n\n",
      "\n",
      "Epoch 1504/9999\n",
      "----------\n",
      "train Loss: 0.4902 Acc: 0.7500\n",
      "has spend time 53m 33s/n\n",
      "val Loss: 0.5423 Acc: 0.7190\n",
      "has spend time 53m 34s/n\n",
      "\n",
      "Epoch 1505/9999\n",
      "----------\n",
      "train Loss: 0.4962 Acc: 0.7131\n",
      "has spend time 53m 35s/n\n",
      "val Loss: 0.5530 Acc: 0.6993\n",
      "has spend time 53m 36s/n\n",
      "\n",
      "Epoch 1506/9999\n",
      "----------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train Loss: 0.5274 Acc: 0.7090\n",
      "has spend time 53m 38s/n\n",
      "val Loss: 0.5523 Acc: 0.7190\n",
      "has spend time 53m 38s/n\n",
      "\n",
      "Epoch 1507/9999\n",
      "----------\n",
      "train Loss: 0.5211 Acc: 0.7049\n",
      "has spend time 53m 40s/n\n",
      "val Loss: 0.5587 Acc: 0.6928\n",
      "has spend time 53m 41s/n\n",
      "\n",
      "Epoch 1508/9999\n",
      "----------\n",
      "train Loss: 0.4832 Acc: 0.7541\n",
      "has spend time 53m 42s/n\n",
      "val Loss: 0.5484 Acc: 0.7059\n",
      "has spend time 53m 43s/n\n",
      "\n",
      "Epoch 1509/9999\n",
      "----------\n",
      "train Loss: 0.5215 Acc: 0.7254\n",
      "has spend time 53m 44s/n\n",
      "val Loss: 0.5438 Acc: 0.7124\n",
      "has spend time 53m 45s/n\n",
      "\n",
      "Epoch 1510/9999\n",
      "----------\n",
      "train Loss: 0.4908 Acc: 0.7418\n",
      "has spend time 53m 46s/n\n",
      "val Loss: 0.5432 Acc: 0.7124\n",
      "has spend time 53m 47s/n\n",
      "\n",
      "Epoch 1511/9999\n",
      "----------\n",
      "train Loss: 0.5397 Acc: 0.7008\n",
      "has spend time 53m 48s/n\n",
      "val Loss: 0.5489 Acc: 0.7059\n",
      "has spend time 53m 49s/n\n",
      "\n",
      "Epoch 1512/9999\n",
      "----------\n",
      "train Loss: 0.5502 Acc: 0.7008\n",
      "has spend time 53m 50s/n\n",
      "val Loss: 0.5537 Acc: 0.7124\n",
      "has spend time 53m 51s/n\n",
      "\n",
      "Epoch 1513/9999\n",
      "----------\n",
      "train Loss: 0.5564 Acc: 0.6967\n",
      "has spend time 53m 53s/n\n",
      "val Loss: 0.5529 Acc: 0.6993\n",
      "has spend time 53m 53s/n\n",
      "\n",
      "Epoch 1514/9999\n",
      "----------\n",
      "train Loss: 0.4795 Acc: 0.7746\n",
      "has spend time 53m 55s/n\n",
      "val Loss: 0.5562 Acc: 0.6993\n",
      "has spend time 53m 55s/n\n",
      "\n",
      "Epoch 1515/9999\n",
      "----------\n",
      "train Loss: 0.5176 Acc: 0.7254\n",
      "has spend time 53m 57s/n\n",
      "val Loss: 0.5394 Acc: 0.7386\n",
      "has spend time 53m 57s/n\n",
      "\n",
      "Epoch 1516/9999\n",
      "----------\n",
      "train Loss: 0.5015 Acc: 0.7254\n",
      "has spend time 53m 59s/n\n",
      "val Loss: 0.5419 Acc: 0.7190\n",
      "has spend time 53m 59s/n\n",
      "\n",
      "Epoch 1517/9999\n",
      "----------\n",
      "train Loss: 0.4933 Acc: 0.7500\n",
      "has spend time 54m 1s/n\n",
      "val Loss: 0.5378 Acc: 0.7190\n",
      "has spend time 54m 1s/n\n",
      "\n",
      "Epoch 1518/9999\n",
      "----------\n",
      "train Loss: 0.5051 Acc: 0.7254\n",
      "has spend time 54m 3s/n\n",
      "val Loss: 0.5421 Acc: 0.7190\n",
      "has spend time 54m 4s/n\n",
      "\n",
      "Epoch 1519/9999\n",
      "----------\n",
      "train Loss: 0.5068 Acc: 0.7459\n",
      "has spend time 54m 5s/n\n",
      "val Loss: 0.5525 Acc: 0.7190\n",
      "has spend time 54m 6s/n\n",
      "\n",
      "Epoch 1520/9999\n",
      "----------\n",
      "train Loss: 0.5011 Acc: 0.7254\n",
      "has spend time 54m 7s/n\n",
      "val Loss: 0.5501 Acc: 0.7124\n",
      "has spend time 54m 8s/n\n",
      "\n",
      "Epoch 1521/9999\n",
      "----------\n",
      "train Loss: 0.4758 Acc: 0.7664\n",
      "has spend time 54m 10s/n\n",
      "val Loss: 0.5450 Acc: 0.7124\n",
      "has spend time 54m 10s/n\n",
      "\n",
      "Epoch 1522/9999\n",
      "----------\n",
      "train Loss: 0.5061 Acc: 0.7582\n",
      "has spend time 54m 12s/n\n",
      "val Loss: 0.5547 Acc: 0.7059\n",
      "has spend time 54m 12s/n\n",
      "\n",
      "Epoch 1523/9999\n",
      "----------\n",
      "train Loss: 0.4905 Acc: 0.7377\n",
      "has spend time 54m 14s/n\n",
      "val Loss: 0.5457 Acc: 0.6993\n",
      "has spend time 54m 14s/n\n",
      "\n",
      "Epoch 1524/9999\n",
      "----------\n",
      "train Loss: 0.5041 Acc: 0.7541\n",
      "has spend time 54m 16s/n\n",
      "val Loss: 0.5373 Acc: 0.7190\n",
      "has spend time 54m 16s/n\n",
      "\n",
      "Epoch 1525/9999\n",
      "----------\n",
      "train Loss: 0.5178 Acc: 0.7336\n",
      "has spend time 54m 18s/n\n",
      "val Loss: 0.5477 Acc: 0.6993\n",
      "has spend time 54m 19s/n\n",
      "\n",
      "Epoch 1526/9999\n",
      "----------\n",
      "train Loss: 0.4949 Acc: 0.7377\n",
      "has spend time 54m 20s/n\n",
      "val Loss: 0.5369 Acc: 0.7124\n",
      "has spend time 54m 21s/n\n",
      "\n",
      "Epoch 1527/9999\n",
      "----------\n",
      "train Loss: 0.4808 Acc: 0.7869\n",
      "has spend time 54m 23s/n\n",
      "val Loss: 0.5466 Acc: 0.7124\n",
      "has spend time 54m 23s/n\n",
      "\n",
      "Epoch 1528/9999\n",
      "----------\n",
      "train Loss: 0.5261 Acc: 0.7008\n",
      "has spend time 54m 25s/n\n",
      "val Loss: 0.5428 Acc: 0.7255\n",
      "has spend time 54m 25s/n\n",
      "\n",
      "Epoch 1529/9999\n",
      "----------\n",
      "train Loss: 0.4711 Acc: 0.7623\n",
      "has spend time 54m 27s/n\n",
      "val Loss: 0.5524 Acc: 0.7059\n",
      "has spend time 54m 28s/n\n",
      "\n",
      "Epoch 1530/9999\n",
      "----------\n",
      "train Loss: 0.5451 Acc: 0.7213\n",
      "has spend time 54m 29s/n\n",
      "val Loss: 0.5432 Acc: 0.7190\n",
      "has spend time 54m 30s/n\n",
      "\n",
      "Epoch 1531/9999\n",
      "----------\n",
      "train Loss: 0.5284 Acc: 0.6967\n",
      "has spend time 54m 32s/n\n",
      "val Loss: 0.5503 Acc: 0.7190\n",
      "has spend time 54m 32s/n\n",
      "\n",
      "Epoch 1532/9999\n",
      "----------\n",
      "train Loss: 0.5127 Acc: 0.7049\n",
      "has spend time 54m 34s/n\n",
      "val Loss: 0.5539 Acc: 0.7059\n",
      "has spend time 54m 34s/n\n",
      "\n",
      "Epoch 1533/9999\n",
      "----------\n",
      "train Loss: 0.4952 Acc: 0.7377\n",
      "has spend time 54m 36s/n\n",
      "val Loss: 0.5507 Acc: 0.7190\n",
      "has spend time 54m 37s/n\n",
      "\n",
      "Epoch 1534/9999\n",
      "----------\n",
      "train Loss: 0.5080 Acc: 0.7336\n",
      "has spend time 54m 38s/n\n",
      "val Loss: 0.5695 Acc: 0.6993\n",
      "has spend time 54m 39s/n\n",
      "\n",
      "Epoch 1535/9999\n",
      "----------\n",
      "train Loss: 0.5019 Acc: 0.7500\n",
      "has spend time 54m 40s/n\n",
      "val Loss: 0.5533 Acc: 0.7059\n",
      "has spend time 54m 41s/n\n",
      "\n",
      "Epoch 1536/9999\n",
      "----------\n",
      "train Loss: 0.5274 Acc: 0.6844\n",
      "has spend time 54m 42s/n\n",
      "val Loss: 0.5566 Acc: 0.6928\n",
      "has spend time 54m 43s/n\n",
      "\n",
      "Epoch 1537/9999\n",
      "----------\n",
      "train Loss: 0.5100 Acc: 0.7295\n",
      "has spend time 54m 44s/n\n",
      "val Loss: 0.5478 Acc: 0.7190\n",
      "has spend time 54m 45s/n\n",
      "\n",
      "Epoch 1538/9999\n",
      "----------\n",
      "train Loss: 0.4897 Acc: 0.7746\n",
      "has spend time 54m 46s/n\n",
      "val Loss: 0.5593 Acc: 0.6993\n",
      "has spend time 54m 47s/n\n",
      "\n",
      "Epoch 1539/9999\n",
      "----------\n",
      "train Loss: 0.5057 Acc: 0.7418\n",
      "has spend time 54m 49s/n\n",
      "val Loss: 0.5453 Acc: 0.7190\n",
      "has spend time 54m 49s/n\n",
      "\n",
      "Epoch 1540/9999\n",
      "----------\n",
      "train Loss: 0.4795 Acc: 0.7541\n",
      "has spend time 54m 51s/n\n",
      "val Loss: 0.5442 Acc: 0.7059\n",
      "has spend time 54m 51s/n\n",
      "\n",
      "Epoch 1541/9999\n",
      "----------\n",
      "train Loss: 0.5072 Acc: 0.7336\n",
      "has spend time 54m 53s/n\n",
      "val Loss: 0.5528 Acc: 0.7059\n",
      "has spend time 54m 53s/n\n",
      "\n",
      "Epoch 1542/9999\n",
      "----------\n",
      "train Loss: 0.4833 Acc: 0.7828\n",
      "has spend time 54m 55s/n\n",
      "val Loss: 0.5468 Acc: 0.7190\n",
      "has spend time 54m 55s/n\n",
      "\n",
      "Epoch 1543/9999\n",
      "----------\n",
      "train Loss: 0.4928 Acc: 0.7418\n",
      "has spend time 54m 57s/n\n",
      "val Loss: 0.5462 Acc: 0.7190\n",
      "has spend time 54m 58s/n\n",
      "\n",
      "Epoch 1544/9999\n",
      "----------\n",
      "train Loss: 0.5024 Acc: 0.7418\n",
      "has spend time 54m 59s/n\n",
      "val Loss: 0.5446 Acc: 0.7320\n",
      "has spend time 54m 60s/n\n",
      "\n",
      "Epoch 1545/9999\n",
      "----------\n",
      "train Loss: 0.5193 Acc: 0.6926\n",
      "has spend time 55m 1s/n\n",
      "val Loss: 0.5453 Acc: 0.7190\n",
      "has spend time 55m 2s/n\n",
      "\n",
      "Epoch 1546/9999\n",
      "----------\n",
      "train Loss: 0.5215 Acc: 0.7418\n",
      "has spend time 55m 4s/n\n",
      "val Loss: 0.5581 Acc: 0.6928\n",
      "has spend time 55m 4s/n\n",
      "\n",
      "Epoch 1547/9999\n",
      "----------\n",
      "train Loss: 0.5046 Acc: 0.7582\n",
      "has spend time 55m 6s/n\n",
      "val Loss: 0.5455 Acc: 0.7190\n",
      "has spend time 55m 7s/n\n",
      "\n",
      "Epoch 1548/9999\n",
      "----------\n",
      "train Loss: 0.5043 Acc: 0.7336\n",
      "has spend time 55m 8s/n\n",
      "val Loss: 0.5422 Acc: 0.7190\n",
      "has spend time 55m 9s/n\n",
      "\n",
      "Epoch 1549/9999\n",
      "----------\n",
      "train Loss: 0.4950 Acc: 0.7664\n",
      "has spend time 55m 10s/n\n",
      "val Loss: 0.5485 Acc: 0.6993\n",
      "has spend time 55m 11s/n\n",
      "\n",
      "Epoch 1550/9999\n",
      "----------\n",
      "train Loss: 0.5168 Acc: 0.7336\n",
      "has spend time 55m 12s/n\n",
      "val Loss: 0.5502 Acc: 0.7124\n",
      "has spend time 55m 13s/n\n",
      "\n",
      "Epoch 1551/9999\n",
      "----------\n",
      "train Loss: 0.5165 Acc: 0.7377\n",
      "has spend time 55m 14s/n\n",
      "val Loss: 0.5475 Acc: 0.7124\n",
      "has spend time 55m 15s/n\n",
      "\n",
      "Epoch 1552/9999\n",
      "----------\n",
      "train Loss: 0.5419 Acc: 0.7377\n",
      "has spend time 55m 17s/n\n",
      "val Loss: 0.5507 Acc: 0.7124\n",
      "has spend time 55m 17s/n\n",
      "\n",
      "Epoch 1553/9999\n",
      "----------\n",
      "train Loss: 0.5070 Acc: 0.7623\n",
      "has spend time 55m 19s/n\n",
      "val Loss: 0.5461 Acc: 0.7190\n",
      "has spend time 55m 20s/n\n",
      "\n",
      "Epoch 1554/9999\n",
      "----------\n",
      "train Loss: 0.4824 Acc: 0.7377\n",
      "has spend time 55m 21s/n\n",
      "val Loss: 0.5510 Acc: 0.7059\n",
      "has spend time 55m 22s/n\n",
      "\n",
      "Epoch 1555/9999\n",
      "----------\n",
      "train Loss: 0.5208 Acc: 0.7541\n",
      "has spend time 55m 23s/n\n",
      "val Loss: 0.5542 Acc: 0.6993\n",
      "has spend time 55m 24s/n\n",
      "\n",
      "Epoch 1556/9999\n",
      "----------\n",
      "train Loss: 0.5234 Acc: 0.7254\n",
      "has spend time 55m 25s/n\n",
      "val Loss: 0.5538 Acc: 0.7059\n",
      "has spend time 55m 26s/n\n",
      "\n",
      "Epoch 1557/9999\n",
      "----------\n",
      "train Loss: 0.4919 Acc: 0.7377\n",
      "has spend time 55m 27s/n\n",
      "val Loss: 0.5512 Acc: 0.7059\n",
      "has spend time 55m 28s/n\n",
      "\n",
      "Epoch 1558/9999\n",
      "----------\n",
      "train Loss: 0.5203 Acc: 0.7418\n",
      "has spend time 55m 29s/n\n",
      "val Loss: 0.5499 Acc: 0.7190\n",
      "has spend time 55m 30s/n\n",
      "\n",
      "Epoch 1559/9999\n",
      "----------\n",
      "train Loss: 0.4986 Acc: 0.7541\n",
      "has spend time 55m 31s/n\n",
      "val Loss: 0.5506 Acc: 0.7124\n",
      "has spend time 55m 32s/n\n",
      "\n",
      "Epoch 1560/9999\n",
      "----------\n",
      "train Loss: 0.5150 Acc: 0.7008\n",
      "has spend time 55m 33s/n\n",
      "val Loss: 0.5405 Acc: 0.7190\n",
      "has spend time 55m 34s/n\n",
      "\n",
      "Epoch 1561/9999\n",
      "----------\n",
      "train Loss: 0.5078 Acc: 0.7295\n",
      "has spend time 55m 36s/n\n",
      "val Loss: 0.5346 Acc: 0.7320\n",
      "has spend time 55m 36s/n\n",
      "\n",
      "Epoch 1562/9999\n",
      "----------\n",
      "train Loss: 0.5247 Acc: 0.7500\n",
      "has spend time 55m 38s/n\n",
      "val Loss: 0.5428 Acc: 0.7059\n",
      "has spend time 55m 39s/n\n",
      "\n",
      "Epoch 1563/9999\n",
      "----------\n",
      "train Loss: 0.5162 Acc: 0.6926\n",
      "has spend time 55m 40s/n\n",
      "val Loss: 0.5472 Acc: 0.7190\n",
      "has spend time 55m 41s/n\n",
      "\n",
      "Epoch 1564/9999\n",
      "----------\n",
      "train Loss: 0.5069 Acc: 0.7746\n",
      "has spend time 55m 42s/n\n",
      "val Loss: 0.5528 Acc: 0.7059\n",
      "has spend time 55m 43s/n\n",
      "\n",
      "Epoch 1565/9999\n",
      "----------\n",
      "train Loss: 0.5240 Acc: 0.7008\n",
      "has spend time 55m 44s/n\n",
      "val Loss: 0.5608 Acc: 0.6928\n",
      "has spend time 55m 45s/n\n",
      "\n",
      "Epoch 1566/9999\n",
      "----------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train Loss: 0.4955 Acc: 0.7623\n",
      "has spend time 55m 46s/n\n",
      "val Loss: 0.5575 Acc: 0.7124\n",
      "has spend time 55m 47s/n\n",
      "\n",
      "Epoch 1567/9999\n",
      "----------\n",
      "train Loss: 0.4901 Acc: 0.7418\n",
      "has spend time 55m 48s/n\n",
      "val Loss: 0.5522 Acc: 0.6928\n",
      "has spend time 55m 49s/n\n",
      "\n",
      "Epoch 1568/9999\n",
      "----------\n",
      "train Loss: 0.5195 Acc: 0.7377\n",
      "has spend time 55m 50s/n\n",
      "val Loss: 0.5511 Acc: 0.7059\n",
      "has spend time 55m 51s/n\n",
      "\n",
      "Epoch 1569/9999\n",
      "----------\n",
      "train Loss: 0.5348 Acc: 0.6516\n",
      "has spend time 55m 53s/n\n",
      "val Loss: 0.5489 Acc: 0.6993\n",
      "has spend time 55m 53s/n\n",
      "\n",
      "Epoch 1570/9999\n",
      "----------\n",
      "train Loss: 0.5079 Acc: 0.7336\n",
      "has spend time 55m 55s/n\n",
      "val Loss: 0.5566 Acc: 0.6993\n",
      "has spend time 55m 55s/n\n",
      "\n",
      "Epoch 1571/9999\n",
      "----------\n",
      "train Loss: 0.4918 Acc: 0.7541\n",
      "has spend time 55m 57s/n\n",
      "val Loss: 0.5546 Acc: 0.7124\n",
      "has spend time 55m 57s/n\n",
      "\n",
      "Epoch 1572/9999\n",
      "----------\n",
      "train Loss: 0.5141 Acc: 0.7336\n",
      "has spend time 55m 59s/n\n",
      "val Loss: 0.5485 Acc: 0.7124\n",
      "has spend time 55m 59s/n\n",
      "\n",
      "Epoch 1573/9999\n",
      "----------\n",
      "train Loss: 0.5479 Acc: 0.7213\n",
      "has spend time 56m 1s/n\n",
      "val Loss: 0.5438 Acc: 0.7124\n",
      "has spend time 56m 1s/n\n",
      "\n",
      "Epoch 1574/9999\n",
      "----------\n",
      "train Loss: 0.5300 Acc: 0.6967\n",
      "has spend time 56m 3s/n\n",
      "val Loss: 0.5456 Acc: 0.6993\n",
      "has spend time 56m 4s/n\n",
      "\n",
      "Epoch 1575/9999\n",
      "----------\n",
      "train Loss: 0.5027 Acc: 0.7500\n",
      "has spend time 56m 5s/n\n",
      "val Loss: 0.5513 Acc: 0.7059\n",
      "has spend time 56m 6s/n\n",
      "\n",
      "Epoch 1576/9999\n",
      "----------\n",
      "train Loss: 0.5212 Acc: 0.7377\n",
      "has spend time 56m 8s/n\n",
      "val Loss: 0.5550 Acc: 0.6863\n",
      "has spend time 56m 8s/n\n",
      "\n",
      "Epoch 1577/9999\n",
      "----------\n",
      "train Loss: 0.4753 Acc: 0.7828\n",
      "has spend time 56m 10s/n\n",
      "val Loss: 0.5659 Acc: 0.6993\n",
      "has spend time 56m 10s/n\n",
      "\n",
      "Epoch 1578/9999\n",
      "----------\n",
      "train Loss: 0.4800 Acc: 0.7664\n",
      "has spend time 56m 12s/n\n",
      "val Loss: 0.5615 Acc: 0.7059\n",
      "has spend time 56m 12s/n\n",
      "\n",
      "Epoch 1579/9999\n",
      "----------\n",
      "train Loss: 0.4968 Acc: 0.7418\n",
      "has spend time 56m 14s/n\n",
      "val Loss: 0.5479 Acc: 0.6928\n",
      "has spend time 56m 14s/n\n",
      "\n",
      "Epoch 1580/9999\n",
      "----------\n",
      "train Loss: 0.5137 Acc: 0.7459\n",
      "has spend time 56m 16s/n\n",
      "val Loss: 0.5527 Acc: 0.7059\n",
      "has spend time 56m 17s/n\n",
      "\n",
      "Epoch 1581/9999\n",
      "----------\n",
      "train Loss: 0.4914 Acc: 0.7541\n",
      "has spend time 56m 18s/n\n",
      "val Loss: 0.5562 Acc: 0.7059\n",
      "has spend time 56m 19s/n\n",
      "\n",
      "Epoch 1582/9999\n",
      "----------\n",
      "train Loss: 0.5216 Acc: 0.7336\n",
      "has spend time 56m 21s/n\n",
      "val Loss: 0.5574 Acc: 0.7059\n",
      "has spend time 56m 21s/n\n",
      "\n",
      "Epoch 1583/9999\n",
      "----------\n",
      "train Loss: 0.5138 Acc: 0.7336\n",
      "has spend time 56m 23s/n\n",
      "val Loss: 0.5517 Acc: 0.7059\n",
      "has spend time 56m 23s/n\n",
      "\n",
      "Epoch 1584/9999\n",
      "----------\n",
      "train Loss: 0.5131 Acc: 0.7500\n",
      "has spend time 56m 25s/n\n",
      "val Loss: 0.5458 Acc: 0.7059\n",
      "has spend time 56m 25s/n\n",
      "\n",
      "Epoch 1585/9999\n",
      "----------\n",
      "train Loss: 0.5094 Acc: 0.7131\n",
      "has spend time 56m 27s/n\n",
      "val Loss: 0.5489 Acc: 0.7124\n",
      "has spend time 56m 27s/n\n",
      "\n",
      "Epoch 1586/9999\n",
      "----------\n",
      "train Loss: 0.5033 Acc: 0.7254\n",
      "has spend time 56m 29s/n\n",
      "val Loss: 0.5701 Acc: 0.6928\n",
      "has spend time 56m 29s/n\n",
      "\n",
      "Epoch 1587/9999\n",
      "----------\n",
      "train Loss: 0.5331 Acc: 0.7090\n",
      "has spend time 56m 31s/n\n",
      "val Loss: 0.5487 Acc: 0.7059\n",
      "has spend time 56m 32s/n\n",
      "\n",
      "Epoch 1588/9999\n",
      "----------\n",
      "train Loss: 0.5048 Acc: 0.7254\n",
      "has spend time 56m 33s/n\n",
      "val Loss: 0.5571 Acc: 0.7059\n",
      "has spend time 56m 34s/n\n",
      "\n",
      "Epoch 1589/9999\n",
      "----------\n",
      "train Loss: 0.4992 Acc: 0.7090\n",
      "has spend time 56m 35s/n\n",
      "val Loss: 0.5558 Acc: 0.6928\n",
      "has spend time 56m 36s/n\n",
      "\n",
      "Epoch 1590/9999\n",
      "----------\n",
      "train Loss: 0.5074 Acc: 0.7705\n",
      "has spend time 56m 38s/n\n",
      "val Loss: 0.5508 Acc: 0.7190\n",
      "has spend time 56m 39s/n\n",
      "\n",
      "Epoch 1591/9999\n",
      "----------\n",
      "train Loss: 0.5075 Acc: 0.7336\n",
      "has spend time 56m 40s/n\n",
      "val Loss: 0.5450 Acc: 0.7124\n",
      "has spend time 56m 41s/n\n",
      "\n",
      "Epoch 1592/9999\n",
      "----------\n",
      "train Loss: 0.5127 Acc: 0.7336\n",
      "has spend time 56m 42s/n\n",
      "val Loss: 0.5506 Acc: 0.7124\n",
      "has spend time 56m 43s/n\n",
      "\n",
      "Epoch 1593/9999\n",
      "----------\n",
      "train Loss: 0.4878 Acc: 0.7582\n",
      "has spend time 56m 44s/n\n",
      "val Loss: 0.5463 Acc: 0.7059\n",
      "has spend time 56m 45s/n\n",
      "\n",
      "Epoch 1594/9999\n",
      "----------\n",
      "train Loss: 0.5066 Acc: 0.7336\n",
      "has spend time 56m 46s/n\n",
      "val Loss: 0.5470 Acc: 0.7124\n",
      "has spend time 56m 47s/n\n",
      "\n",
      "Epoch 1595/9999\n",
      "----------\n",
      "train Loss: 0.5451 Acc: 0.7049\n",
      "has spend time 56m 49s/n\n",
      "val Loss: 0.5535 Acc: 0.6993\n",
      "has spend time 56m 49s/n\n",
      "\n",
      "Epoch 1596/9999\n",
      "----------\n",
      "train Loss: 0.4983 Acc: 0.7746\n",
      "has spend time 56m 51s/n\n",
      "val Loss: 0.5557 Acc: 0.6993\n",
      "has spend time 56m 51s/n\n",
      "\n",
      "Epoch 1597/9999\n",
      "----------\n",
      "train Loss: 0.5114 Acc: 0.7336\n",
      "has spend time 56m 53s/n\n",
      "val Loss: 0.5441 Acc: 0.7255\n",
      "has spend time 56m 53s/n\n",
      "\n",
      "Epoch 1598/9999\n",
      "----------\n",
      "train Loss: 0.5105 Acc: 0.7213\n",
      "has spend time 56m 55s/n\n",
      "val Loss: 0.5470 Acc: 0.7190\n",
      "has spend time 56m 55s/n\n",
      "\n",
      "Epoch 1599/9999\n",
      "----------\n",
      "train Loss: 0.4669 Acc: 0.7828\n",
      "has spend time 56m 57s/n\n",
      "val Loss: 0.5640 Acc: 0.6993\n",
      "has spend time 56m 57s/n\n",
      "\n",
      "Epoch 1600/9999\n",
      "----------\n",
      "train Loss: 0.4963 Acc: 0.7664\n",
      "has spend time 56m 59s/n\n",
      "val Loss: 0.5468 Acc: 0.7124\n",
      "has spend time 56m 60s/n\n",
      "\n",
      "Epoch 1601/9999\n",
      "----------\n",
      "train Loss: 0.4755 Acc: 0.7746\n",
      "has spend time 57m 1s/n\n",
      "val Loss: 0.5431 Acc: 0.7255\n",
      "has spend time 57m 2s/n\n",
      "\n",
      "Epoch 1602/9999\n",
      "----------\n",
      "train Loss: 0.5102 Acc: 0.7336\n",
      "has spend time 57m 4s/n\n",
      "val Loss: 0.5484 Acc: 0.7124\n",
      "has spend time 57m 4s/n\n",
      "\n",
      "Epoch 1603/9999\n",
      "----------\n",
      "train Loss: 0.4985 Acc: 0.7377\n",
      "has spend time 57m 6s/n\n",
      "val Loss: 0.5484 Acc: 0.6928\n",
      "has spend time 57m 6s/n\n",
      "\n",
      "Epoch 1604/9999\n",
      "----------\n",
      "train Loss: 0.5157 Acc: 0.7213\n",
      "has spend time 57m 8s/n\n",
      "val Loss: 0.5468 Acc: 0.7124\n",
      "has spend time 57m 8s/n\n",
      "\n",
      "Epoch 1605/9999\n",
      "----------\n",
      "train Loss: 0.5183 Acc: 0.6926\n",
      "has spend time 57m 10s/n\n",
      "val Loss: 0.5484 Acc: 0.7190\n",
      "has spend time 57m 10s/n\n",
      "\n",
      "Epoch 1606/9999\n",
      "----------\n",
      "train Loss: 0.4970 Acc: 0.7623\n",
      "has spend time 57m 12s/n\n",
      "val Loss: 0.5380 Acc: 0.7124\n",
      "has spend time 57m 13s/n\n",
      "\n",
      "Epoch 1607/9999\n",
      "----------\n",
      "train Loss: 0.5236 Acc: 0.7459\n",
      "has spend time 57m 14s/n\n",
      "val Loss: 0.5457 Acc: 0.6928\n",
      "has spend time 57m 15s/n\n",
      "\n",
      "Epoch 1608/9999\n",
      "----------\n",
      "train Loss: 0.5331 Acc: 0.7131\n",
      "has spend time 57m 16s/n\n",
      "val Loss: 0.5395 Acc: 0.7190\n",
      "has spend time 57m 17s/n\n",
      "\n",
      "Epoch 1609/9999\n",
      "----------\n",
      "train Loss: 0.5491 Acc: 0.7090\n",
      "has spend time 57m 19s/n\n",
      "val Loss: 0.5498 Acc: 0.7124\n",
      "has spend time 57m 19s/n\n",
      "\n",
      "Epoch 1610/9999\n",
      "----------\n",
      "train Loss: 0.4951 Acc: 0.7500\n",
      "has spend time 57m 21s/n\n",
      "val Loss: 0.5464 Acc: 0.7059\n",
      "has spend time 57m 21s/n\n",
      "\n",
      "Epoch 1611/9999\n",
      "----------\n",
      "train Loss: 0.4948 Acc: 0.7377\n",
      "has spend time 57m 23s/n\n",
      "val Loss: 0.5447 Acc: 0.7124\n",
      "has spend time 57m 23s/n\n",
      "\n",
      "Epoch 1612/9999\n",
      "----------\n",
      "train Loss: 0.5043 Acc: 0.7336\n",
      "has spend time 57m 25s/n\n",
      "val Loss: 0.5465 Acc: 0.7059\n",
      "has spend time 57m 25s/n\n",
      "\n",
      "Epoch 1613/9999\n",
      "----------\n",
      "train Loss: 0.5200 Acc: 0.7459\n",
      "has spend time 57m 27s/n\n",
      "val Loss: 0.5554 Acc: 0.6928\n",
      "has spend time 57m 27s/n\n",
      "\n",
      "Epoch 1614/9999\n",
      "----------\n",
      "train Loss: 0.4938 Acc: 0.7582\n",
      "has spend time 57m 29s/n\n",
      "val Loss: 0.5417 Acc: 0.7190\n",
      "has spend time 57m 30s/n\n",
      "\n",
      "Epoch 1615/9999\n",
      "----------\n",
      "train Loss: 0.4887 Acc: 0.7500\n",
      "has spend time 57m 31s/n\n",
      "val Loss: 0.5516 Acc: 0.7124\n",
      "has spend time 57m 32s/n\n",
      "\n",
      "Epoch 1616/9999\n",
      "----------\n",
      "train Loss: 0.4771 Acc: 0.7664\n",
      "has spend time 57m 33s/n\n",
      "val Loss: 0.5547 Acc: 0.7059\n",
      "has spend time 57m 34s/n\n",
      "\n",
      "Epoch 1617/9999\n",
      "----------\n",
      "train Loss: 0.4817 Acc: 0.7705\n",
      "has spend time 57m 35s/n\n",
      "val Loss: 0.5530 Acc: 0.6928\n",
      "has spend time 57m 36s/n\n",
      "\n",
      "Epoch 1618/9999\n",
      "----------\n",
      "train Loss: 0.5084 Acc: 0.7541\n",
      "has spend time 57m 37s/n\n",
      "val Loss: 0.5594 Acc: 0.6993\n",
      "has spend time 57m 38s/n\n",
      "\n",
      "Epoch 1619/9999\n",
      "----------\n",
      "train Loss: 0.5417 Acc: 0.7172\n",
      "has spend time 57m 39s/n\n",
      "val Loss: 0.5517 Acc: 0.7255\n",
      "has spend time 57m 40s/n\n",
      "\n",
      "Epoch 1620/9999\n",
      "----------\n",
      "train Loss: 0.5006 Acc: 0.7459\n",
      "has spend time 57m 41s/n\n",
      "val Loss: 0.5588 Acc: 0.6993\n",
      "has spend time 57m 42s/n\n",
      "\n",
      "Epoch 1621/9999\n",
      "----------\n",
      "train Loss: 0.5095 Acc: 0.7295\n",
      "has spend time 57m 43s/n\n",
      "val Loss: 0.5413 Acc: 0.7255\n",
      "has spend time 57m 44s/n\n",
      "\n",
      "Epoch 1622/9999\n",
      "----------\n",
      "train Loss: 0.4958 Acc: 0.7500\n",
      "has spend time 57m 45s/n\n",
      "val Loss: 0.5357 Acc: 0.7320\n",
      "has spend time 57m 46s/n\n",
      "\n",
      "Epoch 1623/9999\n",
      "----------\n",
      "train Loss: 0.5024 Acc: 0.7377\n",
      "has spend time 57m 48s/n\n",
      "val Loss: 0.5537 Acc: 0.6928\n",
      "has spend time 57m 48s/n\n",
      "\n",
      "Epoch 1624/9999\n",
      "----------\n",
      "train Loss: 0.5344 Acc: 0.7295\n",
      "has spend time 57m 50s/n\n",
      "val Loss: 0.5520 Acc: 0.7059\n",
      "has spend time 57m 51s/n\n",
      "\n",
      "Epoch 1625/9999\n",
      "----------\n",
      "train Loss: 0.5058 Acc: 0.7541\n",
      "has spend time 57m 52s/n\n",
      "val Loss: 0.5701 Acc: 0.6928\n",
      "has spend time 57m 53s/n\n",
      "\n",
      "Epoch 1626/9999\n",
      "----------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train Loss: 0.4866 Acc: 0.7459\n",
      "has spend time 57m 54s/n\n",
      "val Loss: 0.5586 Acc: 0.7059\n",
      "has spend time 57m 55s/n\n",
      "\n",
      "Epoch 1627/9999\n",
      "----------\n",
      "train Loss: 0.5190 Acc: 0.7623\n",
      "has spend time 57m 56s/n\n",
      "val Loss: 0.5448 Acc: 0.7059\n",
      "has spend time 57m 57s/n\n",
      "\n",
      "Epoch 1628/9999\n",
      "----------\n",
      "train Loss: 0.4884 Acc: 0.7541\n",
      "has spend time 57m 58s/n\n",
      "val Loss: 0.5462 Acc: 0.7124\n",
      "has spend time 57m 59s/n\n",
      "\n",
      "Epoch 1629/9999\n",
      "----------\n",
      "train Loss: 0.4822 Acc: 0.7623\n",
      "has spend time 58m 0s/n\n",
      "val Loss: 0.5666 Acc: 0.6928\n",
      "has spend time 58m 1s/n\n",
      "\n",
      "Epoch 1630/9999\n",
      "----------\n",
      "train Loss: 0.4994 Acc: 0.7336\n",
      "has spend time 58m 3s/n\n",
      "val Loss: 0.5545 Acc: 0.6993\n",
      "has spend time 58m 3s/n\n",
      "\n",
      "Epoch 1631/9999\n",
      "----------\n",
      "train Loss: 0.5108 Acc: 0.7828\n",
      "has spend time 58m 5s/n\n",
      "val Loss: 0.5557 Acc: 0.6993\n",
      "has spend time 58m 6s/n\n",
      "\n",
      "Epoch 1632/9999\n",
      "----------\n",
      "train Loss: 0.4979 Acc: 0.7336\n",
      "has spend time 58m 7s/n\n",
      "val Loss: 0.5481 Acc: 0.7190\n",
      "has spend time 58m 8s/n\n",
      "\n",
      "Epoch 1633/9999\n",
      "----------\n",
      "train Loss: 0.4919 Acc: 0.7500\n",
      "has spend time 58m 9s/n\n",
      "val Loss: 0.5527 Acc: 0.7124\n",
      "has spend time 58m 10s/n\n",
      "\n",
      "Epoch 1634/9999\n",
      "----------\n",
      "train Loss: 0.5184 Acc: 0.6967\n",
      "has spend time 58m 11s/n\n",
      "val Loss: 0.5566 Acc: 0.7059\n",
      "has spend time 58m 12s/n\n",
      "\n",
      "Epoch 1635/9999\n",
      "----------\n",
      "train Loss: 0.5193 Acc: 0.7090\n",
      "has spend time 58m 13s/n\n",
      "val Loss: 0.5513 Acc: 0.6928\n",
      "has spend time 58m 14s/n\n",
      "\n",
      "Epoch 1636/9999\n",
      "----------\n",
      "train Loss: 0.5216 Acc: 0.6926\n",
      "has spend time 58m 15s/n\n",
      "val Loss: 0.5507 Acc: 0.6928\n",
      "has spend time 58m 16s/n\n",
      "\n",
      "Epoch 1637/9999\n",
      "----------\n",
      "train Loss: 0.5289 Acc: 0.7541\n",
      "has spend time 58m 17s/n\n",
      "val Loss: 0.5610 Acc: 0.6993\n",
      "has spend time 58m 18s/n\n",
      "\n",
      "Epoch 1638/9999\n",
      "----------\n",
      "train Loss: 0.5050 Acc: 0.7459\n",
      "has spend time 58m 20s/n\n",
      "val Loss: 0.5430 Acc: 0.7124\n",
      "has spend time 58m 20s/n\n",
      "\n",
      "Epoch 1639/9999\n",
      "----------\n",
      "train Loss: 0.5299 Acc: 0.7459\n",
      "has spend time 58m 22s/n\n",
      "val Loss: 0.5439 Acc: 0.7255\n",
      "has spend time 58m 22s/n\n",
      "\n",
      "Epoch 1640/9999\n",
      "----------\n",
      "train Loss: 0.4868 Acc: 0.7910\n",
      "has spend time 58m 24s/n\n",
      "val Loss: 0.5483 Acc: 0.7255\n",
      "has spend time 58m 24s/n\n",
      "\n",
      "Epoch 1641/9999\n",
      "----------\n",
      "train Loss: 0.5287 Acc: 0.7131\n",
      "has spend time 58m 26s/n\n",
      "val Loss: 0.5632 Acc: 0.7059\n",
      "has spend time 58m 26s/n\n",
      "\n",
      "Epoch 1642/9999\n",
      "----------\n",
      "train Loss: 0.5072 Acc: 0.7459\n",
      "has spend time 58m 28s/n\n",
      "val Loss: 0.5495 Acc: 0.7059\n",
      "has spend time 58m 28s/n\n",
      "\n",
      "Epoch 1643/9999\n",
      "----------\n",
      "train Loss: 0.5051 Acc: 0.7500\n",
      "has spend time 58m 30s/n\n",
      "val Loss: 0.5487 Acc: 0.7059\n",
      "has spend time 58m 31s/n\n",
      "\n",
      "Epoch 1644/9999\n",
      "----------\n",
      "train Loss: 0.4792 Acc: 0.7582\n",
      "has spend time 58m 32s/n\n",
      "val Loss: 0.5522 Acc: 0.6928\n",
      "has spend time 58m 33s/n\n",
      "\n",
      "Epoch 1645/9999\n",
      "----------\n",
      "train Loss: 0.4956 Acc: 0.7541\n",
      "has spend time 58m 34s/n\n",
      "val Loss: 0.5486 Acc: 0.6993\n",
      "has spend time 58m 35s/n\n",
      "\n",
      "Epoch 1646/9999\n",
      "----------\n",
      "train Loss: 0.4921 Acc: 0.7787\n",
      "has spend time 58m 37s/n\n",
      "val Loss: 0.5482 Acc: 0.7124\n",
      "has spend time 58m 37s/n\n",
      "\n",
      "Epoch 1647/9999\n",
      "----------\n",
      "train Loss: 0.4929 Acc: 0.7377\n",
      "has spend time 58m 39s/n\n",
      "val Loss: 0.5530 Acc: 0.7124\n",
      "has spend time 58m 40s/n\n",
      "\n",
      "Epoch 1648/9999\n",
      "----------\n",
      "train Loss: 0.4742 Acc: 0.7992\n",
      "has spend time 58m 41s/n\n",
      "val Loss: 0.5631 Acc: 0.6993\n",
      "has spend time 58m 42s/n\n",
      "\n",
      "Epoch 1649/9999\n",
      "----------\n",
      "train Loss: 0.5233 Acc: 0.7131\n",
      "has spend time 58m 43s/n\n",
      "val Loss: 0.5588 Acc: 0.7059\n",
      "has spend time 58m 44s/n\n",
      "\n",
      "Epoch 1650/9999\n",
      "----------\n",
      "train Loss: 0.4649 Acc: 0.7705\n",
      "has spend time 58m 45s/n\n",
      "val Loss: 0.5655 Acc: 0.6928\n",
      "has spend time 58m 46s/n\n",
      "\n",
      "Epoch 1651/9999\n",
      "----------\n",
      "train Loss: 0.5267 Acc: 0.7459\n",
      "has spend time 58m 47s/n\n",
      "val Loss: 0.5552 Acc: 0.7124\n",
      "has spend time 58m 48s/n\n",
      "\n",
      "Epoch 1652/9999\n",
      "----------\n",
      "train Loss: 0.5050 Acc: 0.7418\n",
      "has spend time 58m 49s/n\n",
      "val Loss: 0.5563 Acc: 0.6928\n",
      "has spend time 58m 50s/n\n",
      "\n",
      "Epoch 1653/9999\n",
      "----------\n",
      "train Loss: 0.5142 Acc: 0.7254\n",
      "has spend time 58m 51s/n\n",
      "val Loss: 0.5469 Acc: 0.7190\n",
      "has spend time 58m 52s/n\n",
      "\n",
      "Epoch 1654/9999\n",
      "----------\n",
      "train Loss: 0.5210 Acc: 0.7336\n",
      "has spend time 58m 53s/n\n",
      "val Loss: 0.5421 Acc: 0.7190\n",
      "has spend time 58m 54s/n\n",
      "\n",
      "Epoch 1655/9999\n",
      "----------\n",
      "train Loss: 0.5489 Acc: 0.7295\n",
      "has spend time 58m 55s/n\n",
      "val Loss: 0.5578 Acc: 0.6928\n",
      "has spend time 58m 56s/n\n",
      "\n",
      "Epoch 1656/9999\n",
      "----------\n",
      "train Loss: 0.5046 Acc: 0.7459\n",
      "has spend time 58m 58s/n\n",
      "val Loss: 0.5643 Acc: 0.6928\n",
      "has spend time 58m 58s/n\n",
      "\n",
      "Epoch 1657/9999\n",
      "----------\n",
      "train Loss: 0.5162 Acc: 0.7582\n",
      "has spend time 58m 60s/n\n",
      "val Loss: 0.5504 Acc: 0.7124\n",
      "has spend time 59m 0s/n\n",
      "\n",
      "Epoch 1658/9999\n",
      "----------\n",
      "train Loss: 0.5205 Acc: 0.7541\n",
      "has spend time 59m 2s/n\n",
      "val Loss: 0.5651 Acc: 0.6993\n",
      "has spend time 59m 2s/n\n",
      "\n",
      "Epoch 1659/9999\n",
      "----------\n",
      "train Loss: 0.4764 Acc: 0.7582\n",
      "has spend time 59m 4s/n\n",
      "val Loss: 0.5654 Acc: 0.6993\n",
      "has spend time 59m 5s/n\n",
      "\n",
      "Epoch 1660/9999\n",
      "----------\n",
      "train Loss: 0.5165 Acc: 0.7418\n",
      "has spend time 59m 6s/n\n",
      "val Loss: 0.5455 Acc: 0.7124\n",
      "has spend time 59m 7s/n\n",
      "\n",
      "Epoch 1661/9999\n",
      "----------\n",
      "train Loss: 0.4944 Acc: 0.7746\n",
      "has spend time 59m 8s/n\n",
      "val Loss: 0.5514 Acc: 0.7059\n",
      "has spend time 59m 9s/n\n",
      "\n",
      "Epoch 1662/9999\n",
      "----------\n",
      "train Loss: 0.5098 Acc: 0.7336\n",
      "has spend time 59m 11s/n\n",
      "val Loss: 0.5494 Acc: 0.7124\n",
      "has spend time 59m 11s/n\n",
      "\n",
      "Epoch 1663/9999\n",
      "----------\n",
      "train Loss: 0.4956 Acc: 0.7254\n",
      "has spend time 59m 13s/n\n",
      "val Loss: 0.5505 Acc: 0.7059\n",
      "has spend time 59m 14s/n\n",
      "\n",
      "Epoch 1664/9999\n",
      "----------\n",
      "train Loss: 0.4922 Acc: 0.7295\n",
      "has spend time 59m 15s/n\n",
      "val Loss: 0.5470 Acc: 0.7190\n",
      "has spend time 59m 16s/n\n",
      "\n",
      "Epoch 1665/9999\n",
      "----------\n",
      "train Loss: 0.5196 Acc: 0.7131\n",
      "has spend time 59m 17s/n\n",
      "val Loss: 0.5536 Acc: 0.7059\n",
      "has spend time 59m 18s/n\n",
      "\n",
      "Epoch 1666/9999\n",
      "----------\n",
      "train Loss: 0.5146 Acc: 0.7213\n",
      "has spend time 59m 20s/n\n",
      "val Loss: 0.5471 Acc: 0.7124\n",
      "has spend time 59m 20s/n\n",
      "\n",
      "Epoch 1667/9999\n",
      "----------\n",
      "train Loss: 0.5204 Acc: 0.7418\n",
      "has spend time 59m 22s/n\n",
      "val Loss: 0.5702 Acc: 0.6993\n",
      "has spend time 59m 22s/n\n",
      "\n",
      "Epoch 1668/9999\n",
      "----------\n",
      "train Loss: 0.5569 Acc: 0.7049\n",
      "has spend time 59m 24s/n\n",
      "val Loss: 0.5536 Acc: 0.7190\n",
      "has spend time 59m 24s/n\n",
      "\n",
      "Epoch 1669/9999\n",
      "----------\n",
      "train Loss: 0.5131 Acc: 0.7377\n",
      "has spend time 59m 26s/n\n",
      "val Loss: 0.5670 Acc: 0.7059\n",
      "has spend time 59m 27s/n\n",
      "\n",
      "Epoch 1670/9999\n",
      "----------\n",
      "train Loss: 0.5510 Acc: 0.7131\n",
      "has spend time 59m 28s/n\n",
      "val Loss: 0.5485 Acc: 0.7059\n",
      "has spend time 59m 29s/n\n",
      "\n",
      "Epoch 1671/9999\n",
      "----------\n",
      "train Loss: 0.4985 Acc: 0.7746\n",
      "has spend time 59m 30s/n\n",
      "val Loss: 0.5624 Acc: 0.6993\n",
      "has spend time 59m 31s/n\n",
      "\n",
      "Epoch 1672/9999\n",
      "----------\n",
      "train Loss: 0.5132 Acc: 0.7213\n",
      "has spend time 59m 33s/n\n",
      "val Loss: 0.5458 Acc: 0.7124\n",
      "has spend time 59m 33s/n\n",
      "\n",
      "Epoch 1673/9999\n",
      "----------\n",
      "train Loss: 0.4899 Acc: 0.7623\n",
      "has spend time 59m 35s/n\n",
      "val Loss: 0.5587 Acc: 0.6993\n",
      "has spend time 59m 35s/n\n",
      "\n",
      "Epoch 1674/9999\n",
      "----------\n",
      "train Loss: 0.4921 Acc: 0.7500\n",
      "has spend time 59m 37s/n\n",
      "val Loss: 0.5523 Acc: 0.6993\n",
      "has spend time 59m 37s/n\n",
      "\n",
      "Epoch 1675/9999\n",
      "----------\n",
      "train Loss: 0.4986 Acc: 0.7500\n",
      "has spend time 59m 39s/n\n",
      "val Loss: 0.5450 Acc: 0.7190\n",
      "has spend time 59m 39s/n\n",
      "\n",
      "Epoch 1676/9999\n",
      "----------\n",
      "train Loss: 0.5256 Acc: 0.7295\n",
      "has spend time 59m 41s/n\n",
      "val Loss: 0.5564 Acc: 0.7059\n",
      "has spend time 59m 41s/n\n",
      "\n",
      "Epoch 1677/9999\n",
      "----------\n",
      "train Loss: 0.4773 Acc: 0.7910\n",
      "has spend time 59m 43s/n\n",
      "val Loss: 0.5528 Acc: 0.6993\n",
      "has spend time 59m 44s/n\n",
      "\n",
      "Epoch 1678/9999\n",
      "----------\n",
      "train Loss: 0.5293 Acc: 0.7090\n",
      "has spend time 59m 45s/n\n",
      "val Loss: 0.5560 Acc: 0.7059\n",
      "has spend time 59m 46s/n\n",
      "\n",
      "Epoch 1679/9999\n",
      "----------\n",
      "train Loss: 0.4945 Acc: 0.7336\n",
      "has spend time 59m 48s/n\n",
      "val Loss: 0.5470 Acc: 0.7124\n",
      "has spend time 59m 48s/n\n",
      "\n",
      "Epoch 1680/9999\n",
      "----------\n",
      "train Loss: 0.5249 Acc: 0.7459\n",
      "has spend time 59m 50s/n\n",
      "val Loss: 0.5473 Acc: 0.6993\n",
      "has spend time 59m 50s/n\n",
      "\n",
      "Epoch 1681/9999\n",
      "----------\n",
      "train Loss: 0.4925 Acc: 0.7746\n",
      "has spend time 59m 52s/n\n",
      "val Loss: 0.5499 Acc: 0.7190\n",
      "has spend time 59m 52s/n\n",
      "\n",
      "Epoch 1682/9999\n",
      "----------\n",
      "train Loss: 0.5445 Acc: 0.6885\n",
      "has spend time 59m 54s/n\n",
      "val Loss: 0.5401 Acc: 0.7124\n",
      "has spend time 59m 55s/n\n",
      "\n",
      "Epoch 1683/9999\n",
      "----------\n",
      "train Loss: 0.5013 Acc: 0.7459\n",
      "has spend time 59m 56s/n\n",
      "val Loss: 0.5717 Acc: 0.6993\n",
      "has spend time 59m 57s/n\n",
      "\n",
      "Epoch 1684/9999\n",
      "----------\n",
      "train Loss: 0.5146 Acc: 0.7172\n",
      "has spend time 59m 58s/n\n",
      "val Loss: 0.5438 Acc: 0.7255\n",
      "has spend time 59m 59s/n\n",
      "\n",
      "Epoch 1685/9999\n",
      "----------\n",
      "train Loss: 0.4779 Acc: 0.7582\n",
      "has spend time 60m 0s/n\n",
      "val Loss: 0.5410 Acc: 0.7124\n",
      "has spend time 60m 1s/n\n",
      "\n",
      "Epoch 1686/9999\n",
      "----------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train Loss: 0.4866 Acc: 0.7705\n",
      "has spend time 60m 2s/n\n",
      "val Loss: 0.5570 Acc: 0.7124\n",
      "has spend time 60m 3s/n\n",
      "\n",
      "Epoch 1687/9999\n",
      "----------\n",
      "train Loss: 0.5231 Acc: 0.7418\n",
      "has spend time 60m 4s/n\n",
      "val Loss: 0.5418 Acc: 0.7255\n",
      "has spend time 60m 5s/n\n",
      "\n",
      "Epoch 1688/9999\n",
      "----------\n",
      "train Loss: 0.5163 Acc: 0.7213\n",
      "has spend time 60m 6s/n\n",
      "val Loss: 0.5518 Acc: 0.7124\n",
      "has spend time 60m 7s/n\n",
      "\n",
      "Epoch 1689/9999\n",
      "----------\n",
      "train Loss: 0.5442 Acc: 0.6803\n",
      "has spend time 60m 8s/n\n",
      "val Loss: 0.5527 Acc: 0.7059\n",
      "has spend time 60m 9s/n\n",
      "\n",
      "Epoch 1690/9999\n",
      "----------\n",
      "train Loss: 0.5257 Acc: 0.7295\n",
      "has spend time 60m 11s/n\n",
      "val Loss: 0.5494 Acc: 0.6928\n",
      "has spend time 60m 11s/n\n",
      "\n",
      "Epoch 1691/9999\n",
      "----------\n",
      "train Loss: 0.5048 Acc: 0.7377\n",
      "has spend time 60m 13s/n\n",
      "val Loss: 0.5706 Acc: 0.6993\n",
      "has spend time 60m 14s/n\n",
      "\n",
      "Epoch 1692/9999\n",
      "----------\n",
      "train Loss: 0.5149 Acc: 0.7172\n",
      "has spend time 60m 15s/n\n",
      "val Loss: 0.5544 Acc: 0.6993\n",
      "has spend time 60m 16s/n\n",
      "\n",
      "Epoch 1693/9999\n",
      "----------\n",
      "train Loss: 0.5164 Acc: 0.7582\n",
      "has spend time 60m 17s/n\n",
      "val Loss: 0.5693 Acc: 0.6928\n",
      "has spend time 60m 18s/n\n",
      "\n",
      "Epoch 1694/9999\n",
      "----------\n",
      "train Loss: 0.4986 Acc: 0.7377\n",
      "has spend time 60m 19s/n\n",
      "val Loss: 0.5565 Acc: 0.6928\n",
      "has spend time 60m 20s/n\n",
      "\n",
      "Epoch 1695/9999\n",
      "----------\n",
      "train Loss: 0.5300 Acc: 0.6926\n",
      "has spend time 60m 22s/n\n",
      "val Loss: 0.5528 Acc: 0.6928\n",
      "has spend time 60m 22s/n\n",
      "\n",
      "Epoch 1696/9999\n",
      "----------\n",
      "train Loss: 0.5156 Acc: 0.7254\n",
      "has spend time 60m 24s/n\n",
      "val Loss: 0.5458 Acc: 0.6993\n",
      "has spend time 60m 25s/n\n",
      "\n",
      "Epoch 1697/9999\n",
      "----------\n",
      "train Loss: 0.4695 Acc: 0.7664\n",
      "has spend time 60m 26s/n\n",
      "val Loss: 0.5459 Acc: 0.7059\n",
      "has spend time 60m 27s/n\n",
      "\n",
      "Epoch 1698/9999\n",
      "----------\n",
      "train Loss: 0.4948 Acc: 0.7459\n",
      "has spend time 60m 28s/n\n",
      "val Loss: 0.5504 Acc: 0.7059\n",
      "has spend time 60m 29s/n\n",
      "\n",
      "Epoch 1699/9999\n",
      "----------\n",
      "train Loss: 0.5113 Acc: 0.7254\n",
      "has spend time 60m 30s/n\n",
      "val Loss: 0.5526 Acc: 0.7190\n",
      "has spend time 60m 31s/n\n",
      "\n",
      "Epoch 1700/9999\n",
      "----------\n",
      "train Loss: 0.4978 Acc: 0.7418\n",
      "has spend time 60m 32s/n\n",
      "val Loss: 0.5411 Acc: 0.7255\n",
      "has spend time 60m 33s/n\n",
      "\n",
      "Epoch 1701/9999\n",
      "----------\n",
      "train Loss: 0.4664 Acc: 0.7787\n",
      "has spend time 60m 35s/n\n",
      "val Loss: 0.5598 Acc: 0.7059\n",
      "has spend time 60m 35s/n\n",
      "\n",
      "Epoch 1702/9999\n",
      "----------\n",
      "train Loss: 0.5046 Acc: 0.7131\n",
      "has spend time 60m 37s/n\n",
      "val Loss: 0.5598 Acc: 0.6863\n",
      "has spend time 60m 37s/n\n",
      "\n",
      "Epoch 1703/9999\n",
      "----------\n",
      "train Loss: 0.5106 Acc: 0.7295\n",
      "has spend time 60m 39s/n\n",
      "val Loss: 0.5477 Acc: 0.7255\n",
      "has spend time 60m 39s/n\n",
      "\n",
      "Epoch 1704/9999\n",
      "----------\n",
      "train Loss: 0.4841 Acc: 0.7951\n",
      "has spend time 60m 41s/n\n",
      "val Loss: 0.5581 Acc: 0.7059\n",
      "has spend time 60m 41s/n\n",
      "\n",
      "Epoch 1705/9999\n",
      "----------\n",
      "train Loss: 0.5014 Acc: 0.7418\n",
      "has spend time 60m 43s/n\n",
      "val Loss: 0.5611 Acc: 0.7059\n",
      "has spend time 60m 43s/n\n",
      "\n",
      "Epoch 1706/9999\n",
      "----------\n",
      "train Loss: 0.5123 Acc: 0.7172\n",
      "has spend time 60m 45s/n\n",
      "val Loss: 0.5458 Acc: 0.7190\n",
      "has spend time 60m 45s/n\n",
      "\n",
      "Epoch 1707/9999\n",
      "----------\n",
      "train Loss: 0.5084 Acc: 0.7295\n",
      "has spend time 60m 47s/n\n",
      "val Loss: 0.5544 Acc: 0.7124\n",
      "has spend time 60m 47s/n\n",
      "\n",
      "Epoch 1708/9999\n",
      "----------\n",
      "train Loss: 0.4895 Acc: 0.7500\n",
      "has spend time 60m 49s/n\n",
      "val Loss: 0.5585 Acc: 0.7059\n",
      "has spend time 60m 50s/n\n",
      "\n",
      "Epoch 1709/9999\n",
      "----------\n",
      "train Loss: 0.4976 Acc: 0.7664\n",
      "has spend time 60m 51s/n\n",
      "val Loss: 0.5460 Acc: 0.7124\n",
      "has spend time 60m 52s/n\n",
      "\n",
      "Epoch 1710/9999\n",
      "----------\n",
      "train Loss: 0.5106 Acc: 0.7377\n",
      "has spend time 60m 53s/n\n",
      "val Loss: 0.5459 Acc: 0.7124\n",
      "has spend time 60m 54s/n\n",
      "\n",
      "Epoch 1711/9999\n",
      "----------\n",
      "train Loss: 0.5063 Acc: 0.7541\n",
      "has spend time 60m 55s/n\n",
      "val Loss: 0.5415 Acc: 0.7190\n",
      "has spend time 60m 56s/n\n",
      "\n",
      "Epoch 1712/9999\n",
      "----------\n",
      "train Loss: 0.5042 Acc: 0.7459\n",
      "has spend time 60m 57s/n\n",
      "val Loss: 0.5540 Acc: 0.7124\n",
      "has spend time 60m 58s/n\n",
      "\n",
      "Epoch 1713/9999\n",
      "----------\n",
      "train Loss: 0.4781 Acc: 0.7869\n",
      "has spend time 60m 60s/n\n",
      "val Loss: 0.5527 Acc: 0.7059\n",
      "has spend time 61m 0s/n\n",
      "\n",
      "Epoch 1714/9999\n",
      "----------\n",
      "train Loss: 0.5050 Acc: 0.7213\n",
      "has spend time 61m 2s/n\n",
      "val Loss: 0.5470 Acc: 0.7124\n",
      "has spend time 61m 2s/n\n",
      "\n",
      "Epoch 1715/9999\n",
      "----------\n",
      "train Loss: 0.4964 Acc: 0.7418\n",
      "has spend time 61m 4s/n\n",
      "val Loss: 0.5478 Acc: 0.7059\n",
      "has spend time 61m 5s/n\n",
      "\n",
      "Epoch 1716/9999\n",
      "----------\n",
      "train Loss: 0.5461 Acc: 0.7131\n",
      "has spend time 61m 6s/n\n",
      "val Loss: 0.5479 Acc: 0.7190\n",
      "has spend time 61m 7s/n\n",
      "\n",
      "Epoch 1717/9999\n",
      "----------\n",
      "train Loss: 0.4988 Acc: 0.7254\n",
      "has spend time 61m 8s/n\n",
      "val Loss: 0.5421 Acc: 0.7255\n",
      "has spend time 61m 9s/n\n",
      "\n",
      "Epoch 1718/9999\n",
      "----------\n",
      "train Loss: 0.5207 Acc: 0.7377\n",
      "has spend time 61m 10s/n\n",
      "val Loss: 0.5665 Acc: 0.6993\n",
      "has spend time 61m 11s/n\n",
      "\n",
      "Epoch 1719/9999\n",
      "----------\n",
      "train Loss: 0.4956 Acc: 0.7377\n",
      "has spend time 61m 12s/n\n",
      "val Loss: 0.5645 Acc: 0.6928\n",
      "has spend time 61m 13s/n\n",
      "\n",
      "Epoch 1720/9999\n",
      "----------\n",
      "train Loss: 0.4980 Acc: 0.7582\n",
      "has spend time 61m 14s/n\n",
      "val Loss: 0.5551 Acc: 0.6993\n",
      "has spend time 61m 15s/n\n",
      "\n",
      "Epoch 1721/9999\n",
      "----------\n",
      "train Loss: 0.5042 Acc: 0.7418\n",
      "has spend time 61m 17s/n\n",
      "val Loss: 0.5495 Acc: 0.7124\n",
      "has spend time 61m 17s/n\n",
      "\n",
      "Epoch 1722/9999\n",
      "----------\n",
      "train Loss: 0.5206 Acc: 0.7254\n",
      "has spend time 61m 19s/n\n",
      "val Loss: 0.5559 Acc: 0.6993\n",
      "has spend time 61m 19s/n\n",
      "\n",
      "Epoch 1723/9999\n",
      "----------\n",
      "train Loss: 0.5195 Acc: 0.7418\n",
      "has spend time 61m 21s/n\n",
      "val Loss: 0.5501 Acc: 0.6993\n",
      "has spend time 61m 21s/n\n",
      "\n",
      "Epoch 1724/9999\n",
      "----------\n",
      "train Loss: 0.4987 Acc: 0.7705\n",
      "has spend time 61m 23s/n\n",
      "val Loss: 0.5545 Acc: 0.6928\n",
      "has spend time 61m 23s/n\n",
      "\n",
      "Epoch 1725/9999\n",
      "----------\n",
      "train Loss: 0.5084 Acc: 0.7377\n",
      "has spend time 61m 25s/n\n",
      "val Loss: 0.5619 Acc: 0.6993\n",
      "has spend time 61m 25s/n\n",
      "\n",
      "Epoch 1726/9999\n",
      "----------\n",
      "train Loss: 0.4829 Acc: 0.7500\n",
      "has spend time 61m 27s/n\n",
      "val Loss: 0.5515 Acc: 0.7124\n",
      "has spend time 61m 28s/n\n",
      "\n",
      "Epoch 1727/9999\n",
      "----------\n",
      "train Loss: 0.4946 Acc: 0.7787\n",
      "has spend time 61m 29s/n\n",
      "val Loss: 0.5441 Acc: 0.7124\n",
      "has spend time 61m 30s/n\n",
      "\n",
      "Epoch 1728/9999\n",
      "----------\n",
      "train Loss: 0.4884 Acc: 0.7623\n",
      "has spend time 61m 31s/n\n",
      "val Loss: 0.5476 Acc: 0.7124\n",
      "has spend time 61m 32s/n\n",
      "\n",
      "Epoch 1729/9999\n",
      "----------\n",
      "train Loss: 0.4802 Acc: 0.7418\n",
      "has spend time 61m 33s/n\n",
      "val Loss: 0.5467 Acc: 0.7190\n",
      "has spend time 61m 34s/n\n",
      "\n",
      "Epoch 1730/9999\n",
      "----------\n",
      "train Loss: 0.5171 Acc: 0.7418\n",
      "has spend time 61m 35s/n\n",
      "val Loss: 0.5519 Acc: 0.7059\n",
      "has spend time 61m 36s/n\n",
      "\n",
      "Epoch 1731/9999\n",
      "----------\n",
      "train Loss: 0.5106 Acc: 0.7418\n",
      "has spend time 61m 37s/n\n",
      "val Loss: 0.5633 Acc: 0.6993\n",
      "has spend time 61m 38s/n\n",
      "\n",
      "Epoch 1732/9999\n",
      "----------\n",
      "train Loss: 0.5101 Acc: 0.7582\n",
      "has spend time 61m 39s/n\n",
      "val Loss: 0.5391 Acc: 0.7190\n",
      "has spend time 61m 40s/n\n",
      "\n",
      "Epoch 1733/9999\n",
      "----------\n",
      "train Loss: 0.5104 Acc: 0.7541\n",
      "has spend time 61m 41s/n\n",
      "val Loss: 0.5391 Acc: 0.7124\n",
      "has spend time 61m 42s/n\n",
      "\n",
      "Epoch 1734/9999\n",
      "----------\n",
      "train Loss: 0.5213 Acc: 0.7459\n",
      "has spend time 61m 44s/n\n",
      "val Loss: 0.5488 Acc: 0.7190\n",
      "has spend time 61m 45s/n\n",
      "\n",
      "Epoch 1735/9999\n",
      "----------\n",
      "train Loss: 0.5070 Acc: 0.7131\n",
      "has spend time 61m 46s/n\n",
      "val Loss: 0.5593 Acc: 0.7059\n",
      "has spend time 61m 47s/n\n",
      "\n",
      "Epoch 1736/9999\n",
      "----------\n",
      "train Loss: 0.5103 Acc: 0.7500\n",
      "has spend time 61m 48s/n\n",
      "val Loss: 0.5429 Acc: 0.7190\n",
      "has spend time 61m 49s/n\n",
      "\n",
      "Epoch 1737/9999\n",
      "----------\n",
      "train Loss: 0.4935 Acc: 0.7705\n",
      "has spend time 61m 50s/n\n",
      "val Loss: 0.5534 Acc: 0.7059\n",
      "has spend time 61m 51s/n\n",
      "\n",
      "Epoch 1738/9999\n",
      "----------\n",
      "train Loss: 0.5027 Acc: 0.7254\n",
      "has spend time 61m 52s/n\n",
      "val Loss: 0.5542 Acc: 0.6928\n",
      "has spend time 61m 53s/n\n",
      "\n",
      "Epoch 1739/9999\n",
      "----------\n",
      "train Loss: 0.5206 Acc: 0.7213\n",
      "has spend time 61m 54s/n\n",
      "val Loss: 0.5573 Acc: 0.6993\n",
      "has spend time 61m 55s/n\n",
      "\n",
      "Epoch 1740/9999\n",
      "----------\n",
      "train Loss: 0.4976 Acc: 0.7787\n",
      "has spend time 61m 57s/n\n",
      "val Loss: 0.5410 Acc: 0.7190\n",
      "has spend time 61m 58s/n\n",
      "\n",
      "Epoch 1741/9999\n",
      "----------\n",
      "train Loss: 0.5218 Acc: 0.7254\n",
      "has spend time 61m 59s/n\n",
      "val Loss: 0.5570 Acc: 0.6863\n",
      "has spend time 61m 60s/n\n",
      "\n",
      "Epoch 1742/9999\n",
      "----------\n",
      "train Loss: 0.5469 Acc: 0.6967\n",
      "has spend time 62m 1s/n\n",
      "val Loss: 0.5452 Acc: 0.7124\n",
      "has spend time 62m 2s/n\n",
      "\n",
      "Epoch 1743/9999\n",
      "----------\n",
      "train Loss: 0.5044 Acc: 0.7131\n",
      "has spend time 62m 4s/n\n",
      "val Loss: 0.5490 Acc: 0.7059\n",
      "has spend time 62m 5s/n\n",
      "\n",
      "Epoch 1744/9999\n",
      "----------\n",
      "train Loss: 0.5296 Acc: 0.7131\n",
      "has spend time 62m 6s/n\n",
      "val Loss: 0.5529 Acc: 0.7124\n",
      "has spend time 62m 7s/n\n",
      "\n",
      "Epoch 1745/9999\n",
      "----------\n",
      "train Loss: 0.5288 Acc: 0.7213\n",
      "has spend time 62m 8s/n\n",
      "val Loss: 0.5545 Acc: 0.6993\n",
      "has spend time 62m 9s/n\n",
      "\n",
      "Epoch 1746/9999\n",
      "----------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train Loss: 0.5337 Acc: 0.7459\n",
      "has spend time 62m 10s/n\n",
      "val Loss: 0.5513 Acc: 0.7059\n",
      "has spend time 62m 11s/n\n",
      "\n",
      "Epoch 1747/9999\n",
      "----------\n",
      "train Loss: 0.5084 Acc: 0.7254\n",
      "has spend time 62m 12s/n\n",
      "val Loss: 0.5509 Acc: 0.6993\n",
      "has spend time 62m 13s/n\n",
      "\n",
      "Epoch 1748/9999\n",
      "----------\n",
      "train Loss: 0.4977 Acc: 0.7418\n",
      "has spend time 62m 14s/n\n",
      "val Loss: 0.5508 Acc: 0.7124\n",
      "has spend time 62m 15s/n\n",
      "\n",
      "Epoch 1749/9999\n",
      "----------\n",
      "train Loss: 0.5114 Acc: 0.7213\n",
      "has spend time 62m 17s/n\n",
      "val Loss: 0.5441 Acc: 0.7059\n",
      "has spend time 62m 17s/n\n",
      "\n",
      "Epoch 1750/9999\n",
      "----------\n",
      "train Loss: 0.4803 Acc: 0.7582\n",
      "has spend time 62m 19s/n\n",
      "val Loss: 0.5554 Acc: 0.7124\n",
      "has spend time 62m 20s/n\n",
      "\n",
      "Epoch 1751/9999\n",
      "----------\n",
      "train Loss: 0.5440 Acc: 0.7131\n",
      "has spend time 62m 21s/n\n",
      "val Loss: 0.5691 Acc: 0.7059\n",
      "has spend time 62m 22s/n\n",
      "\n",
      "Epoch 1752/9999\n",
      "----------\n",
      "train Loss: 0.4931 Acc: 0.7664\n",
      "has spend time 62m 23s/n\n",
      "val Loss: 0.5508 Acc: 0.7190\n",
      "has spend time 62m 24s/n\n",
      "\n",
      "Epoch 1753/9999\n",
      "----------\n",
      "train Loss: 0.4908 Acc: 0.7664\n",
      "has spend time 62m 25s/n\n",
      "val Loss: 0.5522 Acc: 0.7059\n",
      "has spend time 62m 26s/n\n",
      "\n",
      "Epoch 1754/9999\n",
      "----------\n",
      "train Loss: 0.4911 Acc: 0.7664\n",
      "has spend time 62m 28s/n\n",
      "val Loss: 0.5509 Acc: 0.7190\n",
      "has spend time 62m 28s/n\n",
      "\n",
      "Epoch 1755/9999\n",
      "----------\n",
      "train Loss: 0.5004 Acc: 0.7664\n",
      "has spend time 62m 30s/n\n",
      "val Loss: 0.5402 Acc: 0.7124\n",
      "has spend time 62m 30s/n\n",
      "\n",
      "Epoch 1756/9999\n",
      "----------\n",
      "train Loss: 0.4810 Acc: 0.7459\n",
      "has spend time 62m 32s/n\n",
      "val Loss: 0.5462 Acc: 0.7059\n",
      "has spend time 62m 32s/n\n",
      "\n",
      "Epoch 1757/9999\n",
      "----------\n",
      "train Loss: 0.5067 Acc: 0.7541\n",
      "has spend time 62m 34s/n\n",
      "val Loss: 0.5424 Acc: 0.7124\n",
      "has spend time 62m 34s/n\n",
      "\n",
      "Epoch 1758/9999\n",
      "----------\n",
      "train Loss: 0.5019 Acc: 0.7213\n",
      "has spend time 62m 36s/n\n",
      "val Loss: 0.5533 Acc: 0.6928\n",
      "has spend time 62m 36s/n\n",
      "\n",
      "Epoch 1759/9999\n",
      "----------\n",
      "train Loss: 0.4959 Acc: 0.7582\n",
      "has spend time 62m 38s/n\n",
      "val Loss: 0.5549 Acc: 0.6993\n",
      "has spend time 62m 38s/n\n",
      "\n",
      "Epoch 1760/9999\n",
      "----------\n",
      "train Loss: 0.5019 Acc: 0.7254\n",
      "has spend time 62m 40s/n\n",
      "val Loss: 0.5705 Acc: 0.6993\n",
      "has spend time 62m 40s/n\n",
      "\n",
      "Epoch 1761/9999\n",
      "----------\n",
      "train Loss: 0.5229 Acc: 0.7172\n",
      "has spend time 62m 42s/n\n",
      "val Loss: 0.5494 Acc: 0.7190\n",
      "has spend time 62m 43s/n\n",
      "\n",
      "Epoch 1762/9999\n",
      "----------\n",
      "train Loss: 0.5097 Acc: 0.7418\n",
      "has spend time 62m 44s/n\n",
      "val Loss: 0.5616 Acc: 0.6993\n",
      "has spend time 62m 45s/n\n",
      "\n",
      "Epoch 1763/9999\n",
      "----------\n",
      "train Loss: 0.5143 Acc: 0.7336\n",
      "has spend time 62m 46s/n\n",
      "val Loss: 0.5464 Acc: 0.7190\n",
      "has spend time 62m 47s/n\n",
      "\n",
      "Epoch 1764/9999\n",
      "----------\n",
      "train Loss: 0.5143 Acc: 0.7172\n",
      "has spend time 62m 48s/n\n",
      "val Loss: 0.5406 Acc: 0.7255\n",
      "has spend time 62m 49s/n\n",
      "\n",
      "Epoch 1765/9999\n",
      "----------\n",
      "train Loss: 0.4939 Acc: 0.7418\n",
      "has spend time 62m 50s/n\n",
      "val Loss: 0.5511 Acc: 0.6993\n",
      "has spend time 62m 51s/n\n",
      "\n",
      "Epoch 1766/9999\n",
      "----------\n",
      "train Loss: 0.4988 Acc: 0.7418\n",
      "has spend time 62m 53s/n\n",
      "val Loss: 0.5536 Acc: 0.7059\n",
      "has spend time 62m 54s/n\n",
      "\n",
      "Epoch 1767/9999\n",
      "----------\n",
      "train Loss: 0.5161 Acc: 0.7213\n",
      "has spend time 62m 55s/n\n",
      "val Loss: 0.5554 Acc: 0.7059\n",
      "has spend time 62m 56s/n\n",
      "\n",
      "Epoch 1768/9999\n",
      "----------\n",
      "train Loss: 0.5240 Acc: 0.7131\n",
      "has spend time 62m 57s/n\n",
      "val Loss: 0.5612 Acc: 0.7059\n",
      "has spend time 62m 58s/n\n",
      "\n",
      "Epoch 1769/9999\n",
      "----------\n",
      "train Loss: 0.5363 Acc: 0.7008\n",
      "has spend time 62m 59s/n\n",
      "val Loss: 0.5576 Acc: 0.7059\n",
      "has spend time 62m 60s/n\n",
      "\n",
      "Epoch 1770/9999\n",
      "----------\n",
      "train Loss: 0.5280 Acc: 0.7336\n",
      "has spend time 63m 1s/n\n",
      "val Loss: 0.5482 Acc: 0.7059\n",
      "has spend time 63m 2s/n\n",
      "\n",
      "Epoch 1771/9999\n",
      "----------\n",
      "train Loss: 0.5081 Acc: 0.7541\n",
      "has spend time 63m 3s/n\n",
      "val Loss: 0.5560 Acc: 0.6928\n",
      "has spend time 63m 4s/n\n",
      "\n",
      "Epoch 1772/9999\n",
      "----------\n",
      "train Loss: 0.5132 Acc: 0.7254\n",
      "has spend time 63m 5s/n\n",
      "val Loss: 0.5427 Acc: 0.7190\n",
      "has spend time 63m 6s/n\n",
      "\n",
      "Epoch 1773/9999\n",
      "----------\n",
      "train Loss: 0.5069 Acc: 0.7459\n",
      "has spend time 63m 7s/n\n",
      "val Loss: 0.5617 Acc: 0.6863\n",
      "has spend time 63m 8s/n\n",
      "\n",
      "Epoch 1774/9999\n",
      "----------\n",
      "train Loss: 0.5259 Acc: 0.7418\n",
      "has spend time 63m 10s/n\n",
      "val Loss: 0.5514 Acc: 0.6993\n",
      "has spend time 63m 10s/n\n",
      "\n",
      "Epoch 1775/9999\n",
      "----------\n",
      "train Loss: 0.5008 Acc: 0.7377\n",
      "has spend time 63m 12s/n\n",
      "val Loss: 0.5656 Acc: 0.6993\n",
      "has spend time 63m 12s/n\n",
      "\n",
      "Epoch 1776/9999\n",
      "----------\n",
      "train Loss: 0.4900 Acc: 0.7664\n",
      "has spend time 63m 14s/n\n",
      "val Loss: 0.5566 Acc: 0.6993\n",
      "has spend time 63m 14s/n\n",
      "\n",
      "Epoch 1777/9999\n",
      "----------\n",
      "train Loss: 0.5036 Acc: 0.7500\n",
      "has spend time 63m 16s/n\n",
      "val Loss: 0.5432 Acc: 0.7255\n",
      "has spend time 63m 16s/n\n",
      "\n",
      "Epoch 1778/9999\n",
      "----------\n",
      "train Loss: 0.4833 Acc: 0.7541\n",
      "has spend time 63m 18s/n\n",
      "val Loss: 0.5422 Acc: 0.7255\n",
      "has spend time 63m 18s/n\n",
      "\n",
      "Epoch 1779/9999\n",
      "----------\n",
      "train Loss: 0.4834 Acc: 0.7213\n",
      "has spend time 63m 20s/n\n",
      "val Loss: 0.5490 Acc: 0.7124\n",
      "has spend time 63m 21s/n\n",
      "\n",
      "Epoch 1780/9999\n",
      "----------\n",
      "train Loss: 0.4973 Acc: 0.7295\n",
      "has spend time 63m 22s/n\n",
      "val Loss: 0.5548 Acc: 0.7124\n",
      "has spend time 63m 23s/n\n",
      "\n",
      "Epoch 1781/9999\n",
      "----------\n",
      "train Loss: 0.5107 Acc: 0.7664\n",
      "has spend time 63m 24s/n\n",
      "val Loss: 0.5502 Acc: 0.7059\n",
      "has spend time 63m 25s/n\n",
      "\n",
      "Epoch 1782/9999\n",
      "----------\n",
      "train Loss: 0.4993 Acc: 0.7295\n",
      "has spend time 63m 26s/n\n",
      "val Loss: 0.5456 Acc: 0.7124\n",
      "has spend time 63m 27s/n\n",
      "\n",
      "Epoch 1783/9999\n",
      "----------\n",
      "train Loss: 0.4780 Acc: 0.7623\n",
      "has spend time 63m 29s/n\n",
      "val Loss: 0.5534 Acc: 0.6993\n",
      "has spend time 63m 30s/n\n",
      "\n",
      "Epoch 1784/9999\n",
      "----------\n",
      "train Loss: 0.4846 Acc: 0.7951\n",
      "has spend time 63m 31s/n\n",
      "val Loss: 0.5614 Acc: 0.6993\n",
      "has spend time 63m 32s/n\n",
      "\n",
      "Epoch 1785/9999\n",
      "----------\n",
      "train Loss: 0.5351 Acc: 0.7336\n",
      "has spend time 63m 33s/n\n",
      "val Loss: 0.5441 Acc: 0.7255\n",
      "has spend time 63m 34s/n\n",
      "\n",
      "Epoch 1786/9999\n",
      "----------\n",
      "train Loss: 0.4775 Acc: 0.7951\n",
      "has spend time 63m 35s/n\n",
      "val Loss: 0.5600 Acc: 0.6993\n",
      "has spend time 63m 36s/n\n",
      "\n",
      "Epoch 1787/9999\n",
      "----------\n",
      "train Loss: 0.4887 Acc: 0.7418\n",
      "has spend time 63m 37s/n\n",
      "val Loss: 0.5480 Acc: 0.7059\n",
      "has spend time 63m 38s/n\n",
      "\n",
      "Epoch 1788/9999\n",
      "----------\n",
      "train Loss: 0.5014 Acc: 0.7459\n",
      "has spend time 63m 40s/n\n",
      "val Loss: 0.5487 Acc: 0.7124\n",
      "has spend time 63m 40s/n\n",
      "\n",
      "Epoch 1789/9999\n",
      "----------\n",
      "train Loss: 0.5052 Acc: 0.7623\n",
      "has spend time 63m 42s/n\n",
      "val Loss: 0.5419 Acc: 0.7190\n",
      "has spend time 63m 43s/n\n",
      "\n",
      "Epoch 1790/9999\n",
      "----------\n",
      "train Loss: 0.5061 Acc: 0.7869\n",
      "has spend time 63m 44s/n\n",
      "val Loss: 0.5507 Acc: 0.7190\n",
      "has spend time 63m 45s/n\n",
      "\n",
      "Epoch 1791/9999\n",
      "----------\n",
      "train Loss: 0.5588 Acc: 0.7049\n",
      "has spend time 63m 46s/n\n",
      "val Loss: 0.5525 Acc: 0.7124\n",
      "has spend time 63m 47s/n\n",
      "\n",
      "Epoch 1792/9999\n",
      "----------\n",
      "train Loss: 0.5307 Acc: 0.7131\n",
      "has spend time 63m 48s/n\n",
      "val Loss: 0.5437 Acc: 0.7190\n",
      "has spend time 63m 49s/n\n",
      "\n",
      "Epoch 1793/9999\n",
      "----------\n",
      "train Loss: 0.5017 Acc: 0.7131\n",
      "has spend time 63m 50s/n\n",
      "val Loss: 0.5484 Acc: 0.7124\n",
      "has spend time 63m 51s/n\n",
      "\n",
      "Epoch 1794/9999\n",
      "----------\n",
      "train Loss: 0.4968 Acc: 0.7623\n",
      "has spend time 63m 52s/n\n",
      "val Loss: 0.5456 Acc: 0.7124\n",
      "has spend time 63m 53s/n\n",
      "\n",
      "Epoch 1795/9999\n",
      "----------\n",
      "train Loss: 0.5354 Acc: 0.7172\n",
      "has spend time 63m 54s/n\n",
      "val Loss: 0.5463 Acc: 0.7059\n",
      "has spend time 63m 55s/n\n",
      "\n",
      "Epoch 1796/9999\n",
      "----------\n",
      "train Loss: 0.5107 Acc: 0.7090\n",
      "has spend time 63m 57s/n\n",
      "val Loss: 0.5420 Acc: 0.7255\n",
      "has spend time 63m 57s/n\n",
      "\n",
      "Epoch 1797/9999\n",
      "----------\n",
      "train Loss: 0.4980 Acc: 0.7336\n",
      "has spend time 63m 59s/n\n",
      "val Loss: 0.5470 Acc: 0.7059\n",
      "has spend time 63m 59s/n\n",
      "\n",
      "Epoch 1798/9999\n",
      "----------\n",
      "train Loss: 0.5250 Acc: 0.7213\n",
      "has spend time 64m 1s/n\n",
      "val Loss: 0.5506 Acc: 0.7059\n",
      "has spend time 64m 1s/n\n",
      "\n",
      "Epoch 1799/9999\n",
      "----------\n",
      "train Loss: 0.4794 Acc: 0.7623\n",
      "has spend time 64m 3s/n\n",
      "val Loss: 0.5471 Acc: 0.7124\n",
      "has spend time 64m 3s/n\n",
      "\n",
      "Epoch 1800/9999\n",
      "----------\n",
      "train Loss: 0.5069 Acc: 0.7541\n",
      "has spend time 64m 5s/n\n",
      "val Loss: 0.5471 Acc: 0.7124\n",
      "has spend time 64m 5s/n\n",
      "\n",
      "Epoch 1801/9999\n",
      "----------\n",
      "train Loss: 0.5060 Acc: 0.7418\n",
      "has spend time 64m 7s/n\n",
      "val Loss: 0.5482 Acc: 0.7059\n",
      "has spend time 64m 7s/n\n",
      "\n",
      "Epoch 1802/9999\n",
      "----------\n",
      "train Loss: 0.5152 Acc: 0.7336\n",
      "has spend time 64m 9s/n\n",
      "val Loss: 0.5692 Acc: 0.6928\n",
      "has spend time 64m 9s/n\n",
      "\n",
      "Epoch 1803/9999\n",
      "----------\n",
      "train Loss: 0.5091 Acc: 0.7131\n",
      "has spend time 64m 11s/n\n",
      "val Loss: 0.5522 Acc: 0.7190\n",
      "has spend time 64m 11s/n\n",
      "\n",
      "Epoch 1804/9999\n",
      "----------\n",
      "train Loss: 0.5144 Acc: 0.7254\n",
      "has spend time 64m 13s/n\n",
      "val Loss: 0.5491 Acc: 0.7190\n",
      "has spend time 64m 14s/n\n",
      "\n",
      "Epoch 1805/9999\n",
      "----------\n",
      "train Loss: 0.5032 Acc: 0.7623\n",
      "has spend time 64m 15s/n\n",
      "val Loss: 0.5555 Acc: 0.7059\n",
      "has spend time 64m 16s/n\n",
      "\n",
      "Epoch 1806/9999\n",
      "----------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train Loss: 0.5222 Acc: 0.7541\n",
      "has spend time 64m 17s/n\n",
      "val Loss: 0.5536 Acc: 0.6993\n",
      "has spend time 64m 18s/n\n",
      "\n",
      "Epoch 1807/9999\n",
      "----------\n",
      "train Loss: 0.5151 Acc: 0.7500\n",
      "has spend time 64m 20s/n\n",
      "val Loss: 0.5414 Acc: 0.7059\n",
      "has spend time 64m 21s/n\n",
      "\n",
      "Epoch 1808/9999\n",
      "----------\n",
      "train Loss: 0.4918 Acc: 0.7582\n",
      "has spend time 64m 22s/n\n",
      "val Loss: 0.5461 Acc: 0.7190\n",
      "has spend time 64m 23s/n\n",
      "\n",
      "Epoch 1809/9999\n",
      "----------\n",
      "train Loss: 0.4907 Acc: 0.7623\n",
      "has spend time 64m 24s/n\n",
      "val Loss: 0.5621 Acc: 0.6797\n",
      "has spend time 64m 25s/n\n",
      "\n",
      "Epoch 1810/9999\n",
      "----------\n",
      "train Loss: 0.4869 Acc: 0.7787\n",
      "has spend time 64m 26s/n\n",
      "val Loss: 0.5528 Acc: 0.6993\n",
      "has spend time 64m 27s/n\n",
      "\n",
      "Epoch 1811/9999\n",
      "----------\n",
      "train Loss: 0.5084 Acc: 0.7254\n",
      "has spend time 64m 28s/n\n",
      "val Loss: 0.5565 Acc: 0.6993\n",
      "has spend time 64m 29s/n\n",
      "\n",
      "Epoch 1812/9999\n",
      "----------\n",
      "train Loss: 0.5170 Acc: 0.7213\n",
      "has spend time 64m 30s/n\n",
      "val Loss: 0.5567 Acc: 0.7059\n",
      "has spend time 64m 31s/n\n",
      "\n",
      "Epoch 1813/9999\n",
      "----------\n",
      "train Loss: 0.5052 Acc: 0.7377\n",
      "has spend time 64m 32s/n\n",
      "val Loss: 0.5486 Acc: 0.7059\n",
      "has spend time 64m 33s/n\n",
      "\n",
      "Epoch 1814/9999\n",
      "----------\n",
      "train Loss: 0.5136 Acc: 0.7213\n",
      "has spend time 64m 34s/n\n",
      "val Loss: 0.5443 Acc: 0.7190\n",
      "has spend time 64m 35s/n\n",
      "\n",
      "Epoch 1815/9999\n",
      "----------\n",
      "train Loss: 0.5399 Acc: 0.7213\n",
      "has spend time 64m 37s/n\n",
      "val Loss: 0.5478 Acc: 0.7190\n",
      "has spend time 64m 37s/n\n",
      "\n",
      "Epoch 1816/9999\n",
      "----------\n",
      "train Loss: 0.5112 Acc: 0.7336\n",
      "has spend time 64m 39s/n\n",
      "val Loss: 0.5506 Acc: 0.7059\n",
      "has spend time 64m 39s/n\n",
      "\n",
      "Epoch 1817/9999\n",
      "----------\n",
      "train Loss: 0.4902 Acc: 0.7336\n",
      "has spend time 64m 41s/n\n",
      "val Loss: 0.5512 Acc: 0.7059\n",
      "has spend time 64m 41s/n\n",
      "\n",
      "Epoch 1818/9999\n",
      "----------\n",
      "train Loss: 0.4884 Acc: 0.7336\n",
      "has spend time 64m 43s/n\n",
      "val Loss: 0.5439 Acc: 0.7059\n",
      "has spend time 64m 43s/n\n",
      "\n",
      "Epoch 1819/9999\n",
      "----------\n",
      "train Loss: 0.4796 Acc: 0.7336\n",
      "has spend time 64m 45s/n\n",
      "val Loss: 0.5445 Acc: 0.7124\n",
      "has spend time 64m 45s/n\n",
      "\n",
      "Epoch 1820/9999\n",
      "----------\n",
      "train Loss: 0.5042 Acc: 0.7459\n",
      "has spend time 64m 47s/n\n",
      "val Loss: 0.5461 Acc: 0.7190\n",
      "has spend time 64m 47s/n\n",
      "\n",
      "Epoch 1821/9999\n",
      "----------\n",
      "train Loss: 0.5382 Acc: 0.7295\n",
      "has spend time 64m 49s/n\n",
      "val Loss: 0.5536 Acc: 0.7059\n",
      "has spend time 64m 49s/n\n",
      "\n",
      "Epoch 1822/9999\n",
      "----------\n",
      "train Loss: 0.4997 Acc: 0.7705\n",
      "has spend time 64m 51s/n\n",
      "val Loss: 0.5492 Acc: 0.7255\n",
      "has spend time 64m 52s/n\n",
      "\n",
      "Epoch 1823/9999\n",
      "----------\n",
      "train Loss: 0.4921 Acc: 0.7582\n",
      "has spend time 64m 53s/n\n",
      "val Loss: 0.5435 Acc: 0.7124\n",
      "has spend time 64m 54s/n\n",
      "\n",
      "Epoch 1824/9999\n",
      "----------\n",
      "train Loss: 0.5107 Acc: 0.7377\n",
      "has spend time 64m 56s/n\n",
      "val Loss: 0.5571 Acc: 0.6993\n",
      "has spend time 64m 56s/n\n",
      "\n",
      "Epoch 1825/9999\n",
      "----------\n",
      "train Loss: 0.4942 Acc: 0.7459\n",
      "has spend time 64m 58s/n\n",
      "val Loss: 0.5444 Acc: 0.6993\n",
      "has spend time 64m 58s/n\n",
      "\n",
      "Epoch 1826/9999\n",
      "----------\n",
      "train Loss: 0.5203 Acc: 0.7295\n",
      "has spend time 64m 60s/n\n",
      "val Loss: 0.5716 Acc: 0.6863\n",
      "has spend time 65m 1s/n\n",
      "\n",
      "Epoch 1827/9999\n",
      "----------\n",
      "train Loss: 0.5290 Acc: 0.7746\n",
      "has spend time 65m 2s/n\n",
      "val Loss: 0.5557 Acc: 0.6928\n",
      "has spend time 65m 3s/n\n",
      "\n",
      "Epoch 1828/9999\n",
      "----------\n",
      "train Loss: 0.5180 Acc: 0.7500\n",
      "has spend time 65m 4s/n\n",
      "val Loss: 0.5710 Acc: 0.6993\n",
      "has spend time 65m 5s/n\n",
      "\n",
      "Epoch 1829/9999\n",
      "----------\n",
      "train Loss: 0.4844 Acc: 0.7705\n",
      "has spend time 65m 6s/n\n",
      "val Loss: 0.5502 Acc: 0.7190\n",
      "has spend time 65m 7s/n\n",
      "\n",
      "Epoch 1830/9999\n",
      "----------\n",
      "train Loss: 0.5279 Acc: 0.7336\n",
      "has spend time 65m 9s/n\n",
      "val Loss: 0.5530 Acc: 0.7190\n",
      "has spend time 65m 9s/n\n",
      "\n",
      "Epoch 1831/9999\n",
      "----------\n",
      "train Loss: 0.4958 Acc: 0.7705\n",
      "has spend time 65m 11s/n\n",
      "val Loss: 0.5588 Acc: 0.6993\n",
      "has spend time 65m 11s/n\n",
      "\n",
      "Epoch 1832/9999\n",
      "----------\n",
      "train Loss: 0.5050 Acc: 0.7131\n",
      "has spend time 65m 13s/n\n",
      "val Loss: 0.5551 Acc: 0.7059\n",
      "has spend time 65m 13s/n\n",
      "\n",
      "Epoch 1833/9999\n",
      "----------\n",
      "train Loss: 0.4881 Acc: 0.7500\n",
      "has spend time 65m 15s/n\n",
      "val Loss: 0.5536 Acc: 0.7124\n",
      "has spend time 65m 15s/n\n",
      "\n",
      "Epoch 1834/9999\n",
      "----------\n",
      "train Loss: 0.5296 Acc: 0.7049\n",
      "has spend time 65m 17s/n\n",
      "val Loss: 0.5577 Acc: 0.7059\n",
      "has spend time 65m 17s/n\n",
      "\n",
      "Epoch 1835/9999\n",
      "----------\n",
      "train Loss: 0.5081 Acc: 0.7336\n",
      "has spend time 65m 19s/n\n",
      "val Loss: 0.5487 Acc: 0.7255\n",
      "has spend time 65m 19s/n\n",
      "\n",
      "Epoch 1836/9999\n",
      "----------\n",
      "train Loss: 0.5035 Acc: 0.7459\n",
      "has spend time 65m 21s/n\n",
      "val Loss: 0.5520 Acc: 0.7059\n",
      "has spend time 65m 21s/n\n",
      "\n",
      "Epoch 1837/9999\n",
      "----------\n",
      "train Loss: 0.5224 Acc: 0.7377\n",
      "has spend time 65m 23s/n\n",
      "val Loss: 0.5553 Acc: 0.6993\n",
      "has spend time 65m 24s/n\n",
      "\n",
      "Epoch 1838/9999\n",
      "----------\n",
      "train Loss: 0.4966 Acc: 0.7418\n",
      "has spend time 65m 25s/n\n",
      "val Loss: 0.5551 Acc: 0.7190\n",
      "has spend time 65m 26s/n\n",
      "\n",
      "Epoch 1839/9999\n",
      "----------\n",
      "train Loss: 0.4805 Acc: 0.7746\n",
      "has spend time 65m 27s/n\n",
      "val Loss: 0.5473 Acc: 0.7124\n",
      "has spend time 65m 28s/n\n",
      "\n",
      "Epoch 1840/9999\n",
      "----------\n",
      "train Loss: 0.5111 Acc: 0.7090\n",
      "has spend time 65m 29s/n\n",
      "val Loss: 0.5547 Acc: 0.7059\n",
      "has spend time 65m 30s/n\n",
      "\n",
      "Epoch 1841/9999\n",
      "----------\n",
      "train Loss: 0.4887 Acc: 0.7295\n",
      "has spend time 65m 31s/n\n",
      "val Loss: 0.5691 Acc: 0.6993\n",
      "has spend time 65m 32s/n\n",
      "\n",
      "Epoch 1842/9999\n",
      "----------\n",
      "train Loss: 0.5308 Acc: 0.7008\n",
      "has spend time 65m 33s/n\n",
      "val Loss: 0.5528 Acc: 0.6993\n",
      "has spend time 65m 34s/n\n",
      "\n",
      "Epoch 1843/9999\n",
      "----------\n",
      "train Loss: 0.4940 Acc: 0.7500\n",
      "has spend time 65m 35s/n\n",
      "val Loss: 0.5451 Acc: 0.7059\n",
      "has spend time 65m 36s/n\n",
      "\n",
      "Epoch 1844/9999\n",
      "----------\n",
      "train Loss: 0.5044 Acc: 0.7664\n",
      "has spend time 65m 38s/n\n",
      "val Loss: 0.5576 Acc: 0.7255\n",
      "has spend time 65m 38s/n\n",
      "\n",
      "Epoch 1845/9999\n",
      "----------\n",
      "train Loss: 0.5318 Acc: 0.7336\n",
      "has spend time 65m 40s/n\n",
      "val Loss: 0.5577 Acc: 0.7059\n",
      "has spend time 65m 41s/n\n",
      "\n",
      "Epoch 1846/9999\n",
      "----------\n",
      "train Loss: 0.5224 Acc: 0.7336\n",
      "has spend time 65m 42s/n\n",
      "val Loss: 0.5557 Acc: 0.6993\n",
      "has spend time 65m 43s/n\n",
      "\n",
      "Epoch 1847/9999\n",
      "----------\n",
      "train Loss: 0.4808 Acc: 0.7582\n",
      "has spend time 65m 44s/n\n",
      "val Loss: 0.5419 Acc: 0.7124\n",
      "has spend time 65m 45s/n\n",
      "\n",
      "Epoch 1848/9999\n",
      "----------\n",
      "train Loss: 0.4840 Acc: 0.7377\n",
      "has spend time 65m 46s/n\n",
      "val Loss: 0.5504 Acc: 0.7059\n",
      "has spend time 65m 47s/n\n",
      "\n",
      "Epoch 1849/9999\n",
      "----------\n",
      "train Loss: 0.5039 Acc: 0.7500\n",
      "has spend time 65m 48s/n\n",
      "val Loss: 0.5552 Acc: 0.7059\n",
      "has spend time 65m 49s/n\n",
      "\n",
      "Epoch 1850/9999\n",
      "----------\n",
      "train Loss: 0.5185 Acc: 0.7377\n",
      "has spend time 65m 51s/n\n",
      "val Loss: 0.5469 Acc: 0.7124\n",
      "has spend time 65m 52s/n\n",
      "\n",
      "Epoch 1851/9999\n",
      "----------\n",
      "train Loss: 0.4835 Acc: 0.7459\n",
      "has spend time 65m 53s/n\n",
      "val Loss: 0.5445 Acc: 0.7124\n",
      "has spend time 65m 54s/n\n",
      "\n",
      "Epoch 1852/9999\n",
      "----------\n",
      "train Loss: 0.5094 Acc: 0.7459\n",
      "has spend time 65m 55s/n\n",
      "val Loss: 0.5485 Acc: 0.7124\n",
      "has spend time 65m 56s/n\n",
      "\n",
      "Epoch 1853/9999\n",
      "----------\n",
      "train Loss: 0.4993 Acc: 0.7459\n",
      "has spend time 65m 57s/n\n",
      "val Loss: 0.5488 Acc: 0.7124\n",
      "has spend time 65m 58s/n\n",
      "\n",
      "Epoch 1854/9999\n",
      "----------\n",
      "train Loss: 0.5120 Acc: 0.7336\n",
      "has spend time 65m 60s/n\n",
      "val Loss: 0.5657 Acc: 0.6928\n",
      "has spend time 66m 1s/n\n",
      "\n",
      "Epoch 1855/9999\n",
      "----------\n",
      "train Loss: 0.5080 Acc: 0.7213\n",
      "has spend time 66m 2s/n\n",
      "val Loss: 0.5608 Acc: 0.6993\n",
      "has spend time 66m 3s/n\n",
      "\n",
      "Epoch 1856/9999\n",
      "----------\n",
      "train Loss: 0.5248 Acc: 0.7172\n",
      "has spend time 66m 4s/n\n",
      "val Loss: 0.5528 Acc: 0.7124\n",
      "has spend time 66m 5s/n\n",
      "\n",
      "Epoch 1857/9999\n",
      "----------\n",
      "train Loss: 0.4999 Acc: 0.7336\n",
      "has spend time 66m 6s/n\n",
      "val Loss: 0.5491 Acc: 0.7190\n",
      "has spend time 66m 7s/n\n",
      "\n",
      "Epoch 1858/9999\n",
      "----------\n",
      "train Loss: 0.5188 Acc: 0.7213\n",
      "has spend time 66m 8s/n\n",
      "val Loss: 0.5558 Acc: 0.6928\n",
      "has spend time 66m 9s/n\n",
      "\n",
      "Epoch 1859/9999\n",
      "----------\n",
      "train Loss: 0.5496 Acc: 0.7008\n",
      "has spend time 66m 11s/n\n",
      "val Loss: 0.5602 Acc: 0.6993\n",
      "has spend time 66m 11s/n\n",
      "\n",
      "Epoch 1860/9999\n",
      "----------\n",
      "train Loss: 0.5244 Acc: 0.7418\n",
      "has spend time 66m 13s/n\n",
      "val Loss: 0.5669 Acc: 0.6993\n",
      "has spend time 66m 13s/n\n",
      "\n",
      "Epoch 1861/9999\n",
      "----------\n",
      "train Loss: 0.5036 Acc: 0.7623\n",
      "has spend time 66m 15s/n\n",
      "val Loss: 0.5662 Acc: 0.7059\n",
      "has spend time 66m 15s/n\n",
      "\n",
      "Epoch 1862/9999\n",
      "----------\n",
      "train Loss: 0.5175 Acc: 0.7131\n",
      "has spend time 66m 17s/n\n",
      "val Loss: 0.5567 Acc: 0.7124\n",
      "has spend time 66m 17s/n\n",
      "\n",
      "Epoch 1863/9999\n",
      "----------\n",
      "train Loss: 0.4788 Acc: 0.7418\n",
      "has spend time 66m 19s/n\n",
      "val Loss: 0.5519 Acc: 0.7059\n",
      "has spend time 66m 20s/n\n",
      "\n",
      "Epoch 1864/9999\n",
      "----------\n",
      "train Loss: 0.5248 Acc: 0.7254\n",
      "has spend time 66m 21s/n\n",
      "val Loss: 0.5501 Acc: 0.7190\n",
      "has spend time 66m 22s/n\n",
      "\n",
      "Epoch 1865/9999\n",
      "----------\n",
      "train Loss: 0.4919 Acc: 0.7377\n",
      "has spend time 66m 24s/n\n",
      "val Loss: 0.5637 Acc: 0.6993\n",
      "has spend time 66m 24s/n\n",
      "\n",
      "Epoch 1866/9999\n",
      "----------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train Loss: 0.5377 Acc: 0.7090\n",
      "has spend time 66m 26s/n\n",
      "val Loss: 0.5529 Acc: 0.7124\n",
      "has spend time 66m 26s/n\n",
      "\n",
      "Epoch 1867/9999\n",
      "----------\n",
      "train Loss: 0.5283 Acc: 0.7254\n",
      "has spend time 66m 28s/n\n",
      "val Loss: 0.5554 Acc: 0.7059\n",
      "has spend time 66m 28s/n\n",
      "\n",
      "Epoch 1868/9999\n",
      "----------\n",
      "train Loss: 0.4762 Acc: 0.7623\n",
      "has spend time 66m 30s/n\n",
      "val Loss: 0.5501 Acc: 0.7190\n",
      "has spend time 66m 30s/n\n",
      "\n",
      "Epoch 1869/9999\n",
      "----------\n",
      "train Loss: 0.5029 Acc: 0.7418\n",
      "has spend time 66m 32s/n\n",
      "val Loss: 0.5496 Acc: 0.7124\n",
      "has spend time 66m 32s/n\n",
      "\n",
      "Epoch 1870/9999\n",
      "----------\n",
      "train Loss: 0.5423 Acc: 0.7213\n",
      "has spend time 66m 34s/n\n",
      "val Loss: 0.5550 Acc: 0.6993\n",
      "has spend time 66m 34s/n\n",
      "\n",
      "Epoch 1871/9999\n",
      "----------\n",
      "train Loss: 0.5134 Acc: 0.7336\n",
      "has spend time 66m 36s/n\n",
      "val Loss: 0.5676 Acc: 0.6928\n",
      "has spend time 66m 37s/n\n",
      "\n",
      "Epoch 1872/9999\n",
      "----------\n",
      "train Loss: 0.5044 Acc: 0.7459\n",
      "has spend time 66m 38s/n\n",
      "val Loss: 0.5437 Acc: 0.7190\n",
      "has spend time 66m 39s/n\n",
      "\n",
      "Epoch 1873/9999\n",
      "----------\n",
      "train Loss: 0.5079 Acc: 0.7336\n",
      "has spend time 66m 40s/n\n",
      "val Loss: 0.5477 Acc: 0.6928\n",
      "has spend time 66m 41s/n\n",
      "\n",
      "Epoch 1874/9999\n",
      "----------\n",
      "train Loss: 0.5114 Acc: 0.7254\n",
      "has spend time 66m 42s/n\n",
      "val Loss: 0.5459 Acc: 0.6993\n",
      "has spend time 66m 43s/n\n",
      "\n",
      "Epoch 1875/9999\n",
      "----------\n",
      "train Loss: 0.4898 Acc: 0.7746\n",
      "has spend time 66m 45s/n\n",
      "val Loss: 0.5450 Acc: 0.7059\n",
      "has spend time 66m 45s/n\n",
      "\n",
      "Epoch 1876/9999\n",
      "----------\n",
      "train Loss: 0.5052 Acc: 0.7582\n",
      "has spend time 66m 47s/n\n",
      "val Loss: 0.5513 Acc: 0.7059\n",
      "has spend time 66m 47s/n\n",
      "\n",
      "Epoch 1877/9999\n",
      "----------\n",
      "train Loss: 0.4913 Acc: 0.7254\n",
      "has spend time 66m 49s/n\n",
      "val Loss: 0.5451 Acc: 0.7124\n",
      "has spend time 66m 49s/n\n",
      "\n",
      "Epoch 1878/9999\n",
      "----------\n",
      "train Loss: 0.4908 Acc: 0.7418\n",
      "has spend time 66m 51s/n\n",
      "val Loss: 0.5552 Acc: 0.7059\n",
      "has spend time 66m 52s/n\n",
      "\n",
      "Epoch 1879/9999\n",
      "----------\n",
      "train Loss: 0.4885 Acc: 0.7664\n",
      "has spend time 66m 53s/n\n",
      "val Loss: 0.5498 Acc: 0.6993\n",
      "has spend time 66m 54s/n\n",
      "\n",
      "Epoch 1880/9999\n",
      "----------\n",
      "train Loss: 0.4875 Acc: 0.7377\n",
      "has spend time 66m 55s/n\n",
      "val Loss: 0.5359 Acc: 0.7190\n",
      "has spend time 66m 56s/n\n",
      "\n",
      "Epoch 1881/9999\n",
      "----------\n",
      "train Loss: 0.5132 Acc: 0.7254\n",
      "has spend time 66m 58s/n\n",
      "val Loss: 0.5418 Acc: 0.7124\n",
      "has spend time 66m 58s/n\n",
      "\n",
      "Epoch 1882/9999\n",
      "----------\n",
      "train Loss: 0.5455 Acc: 0.7213\n",
      "has spend time 66m 60s/n\n",
      "val Loss: 0.5510 Acc: 0.7124\n",
      "has spend time 67m 0s/n\n",
      "\n",
      "Epoch 1883/9999\n",
      "----------\n",
      "train Loss: 0.5019 Acc: 0.7213\n",
      "has spend time 67m 2s/n\n",
      "val Loss: 0.5497 Acc: 0.7190\n",
      "has spend time 67m 2s/n\n",
      "\n",
      "Epoch 1884/9999\n",
      "----------\n",
      "train Loss: 0.5439 Acc: 0.7090\n",
      "has spend time 67m 4s/n\n",
      "val Loss: 0.5590 Acc: 0.6993\n",
      "has spend time 67m 4s/n\n",
      "\n",
      "Epoch 1885/9999\n",
      "----------\n",
      "train Loss: 0.5279 Acc: 0.7172\n",
      "has spend time 67m 6s/n\n",
      "val Loss: 0.5489 Acc: 0.7190\n",
      "has spend time 67m 6s/n\n",
      "\n",
      "Epoch 1886/9999\n",
      "----------\n",
      "train Loss: 0.5210 Acc: 0.7131\n",
      "has spend time 67m 8s/n\n",
      "val Loss: 0.5386 Acc: 0.7255\n",
      "has spend time 67m 9s/n\n",
      "\n",
      "Epoch 1887/9999\n",
      "----------\n",
      "train Loss: 0.4885 Acc: 0.7746\n",
      "has spend time 67m 10s/n\n",
      "val Loss: 0.5495 Acc: 0.7059\n",
      "has spend time 67m 11s/n\n",
      "\n",
      "Epoch 1888/9999\n",
      "----------\n",
      "train Loss: 0.4896 Acc: 0.7787\n",
      "has spend time 67m 12s/n\n",
      "val Loss: 0.5553 Acc: 0.6993\n",
      "has spend time 67m 13s/n\n",
      "\n",
      "Epoch 1889/9999\n",
      "----------\n",
      "train Loss: 0.5355 Acc: 0.6844\n",
      "has spend time 67m 14s/n\n",
      "val Loss: 0.5551 Acc: 0.7059\n",
      "has spend time 67m 15s/n\n",
      "\n",
      "Epoch 1890/9999\n",
      "----------\n",
      "train Loss: 0.5063 Acc: 0.7213\n",
      "has spend time 67m 16s/n\n",
      "val Loss: 0.5533 Acc: 0.7124\n",
      "has spend time 67m 17s/n\n",
      "\n",
      "Epoch 1891/9999\n",
      "----------\n",
      "train Loss: 0.5242 Acc: 0.7049\n",
      "has spend time 67m 18s/n\n",
      "val Loss: 0.5525 Acc: 0.7059\n",
      "has spend time 67m 19s/n\n",
      "\n",
      "Epoch 1892/9999\n",
      "----------\n",
      "train Loss: 0.4944 Acc: 0.7541\n",
      "has spend time 67m 20s/n\n",
      "val Loss: 0.5525 Acc: 0.7059\n",
      "has spend time 67m 21s/n\n",
      "\n",
      "Epoch 1893/9999\n",
      "----------\n",
      "train Loss: 0.5540 Acc: 0.7213\n",
      "has spend time 67m 22s/n\n",
      "val Loss: 0.5433 Acc: 0.7124\n",
      "has spend time 67m 23s/n\n",
      "\n",
      "Epoch 1894/9999\n",
      "----------\n",
      "train Loss: 0.4962 Acc: 0.7623\n",
      "has spend time 67m 24s/n\n",
      "val Loss: 0.5440 Acc: 0.7059\n",
      "has spend time 67m 25s/n\n",
      "\n",
      "Epoch 1895/9999\n",
      "----------\n",
      "train Loss: 0.5293 Acc: 0.7213\n",
      "has spend time 67m 27s/n\n",
      "val Loss: 0.5514 Acc: 0.6928\n",
      "has spend time 67m 27s/n\n",
      "\n",
      "Epoch 1896/9999\n",
      "----------\n",
      "train Loss: 0.4839 Acc: 0.7582\n",
      "has spend time 67m 29s/n\n",
      "val Loss: 0.5484 Acc: 0.7124\n",
      "has spend time 67m 29s/n\n",
      "\n",
      "Epoch 1897/9999\n",
      "----------\n",
      "train Loss: 0.4842 Acc: 0.7787\n",
      "has spend time 67m 31s/n\n",
      "val Loss: 0.5531 Acc: 0.7059\n",
      "has spend time 67m 32s/n\n",
      "\n",
      "Epoch 1898/9999\n",
      "----------\n",
      "train Loss: 0.4907 Acc: 0.7459\n",
      "has spend time 67m 33s/n\n",
      "val Loss: 0.5494 Acc: 0.7124\n",
      "has spend time 67m 34s/n\n",
      "\n",
      "Epoch 1899/9999\n",
      "----------\n",
      "train Loss: 0.5193 Acc: 0.7295\n",
      "has spend time 67m 35s/n\n",
      "val Loss: 0.5605 Acc: 0.6928\n",
      "has spend time 67m 36s/n\n",
      "\n",
      "Epoch 1900/9999\n",
      "----------\n",
      "train Loss: 0.4901 Acc: 0.7500\n",
      "has spend time 67m 37s/n\n",
      "val Loss: 0.5480 Acc: 0.7059\n",
      "has spend time 67m 38s/n\n",
      "\n",
      "Epoch 1901/9999\n",
      "----------\n",
      "train Loss: 0.4846 Acc: 0.7418\n",
      "has spend time 67m 39s/n\n",
      "val Loss: 0.5506 Acc: 0.7124\n",
      "has spend time 67m 40s/n\n",
      "\n",
      "Epoch 1902/9999\n",
      "----------\n",
      "train Loss: 0.5034 Acc: 0.7377\n",
      "has spend time 67m 42s/n\n",
      "val Loss: 0.5510 Acc: 0.7059\n",
      "has spend time 67m 42s/n\n",
      "\n",
      "Epoch 1903/9999\n",
      "----------\n",
      "train Loss: 0.5128 Acc: 0.7418\n",
      "has spend time 67m 44s/n\n",
      "val Loss: 0.5519 Acc: 0.7124\n",
      "has spend time 67m 45s/n\n",
      "\n",
      "Epoch 1904/9999\n",
      "----------\n",
      "train Loss: 0.5199 Acc: 0.7336\n",
      "has spend time 67m 46s/n\n",
      "val Loss: 0.5596 Acc: 0.6797\n",
      "has spend time 67m 47s/n\n",
      "\n",
      "Epoch 1905/9999\n",
      "----------\n",
      "train Loss: 0.5118 Acc: 0.7377\n",
      "has spend time 67m 48s/n\n",
      "val Loss: 0.5738 Acc: 0.6928\n",
      "has spend time 67m 49s/n\n",
      "\n",
      "Epoch 1906/9999\n",
      "----------\n",
      "train Loss: 0.5038 Acc: 0.7623\n",
      "has spend time 67m 50s/n\n",
      "val Loss: 0.5493 Acc: 0.7059\n",
      "has spend time 67m 51s/n\n",
      "\n",
      "Epoch 1907/9999\n",
      "----------\n",
      "train Loss: 0.5000 Acc: 0.7295\n",
      "has spend time 67m 52s/n\n",
      "val Loss: 0.5538 Acc: 0.6993\n",
      "has spend time 67m 53s/n\n",
      "\n",
      "Epoch 1908/9999\n",
      "----------\n",
      "train Loss: 0.5133 Acc: 0.7049\n",
      "has spend time 67m 55s/n\n",
      "val Loss: 0.5580 Acc: 0.7124\n",
      "has spend time 67m 55s/n\n",
      "\n",
      "Epoch 1909/9999\n",
      "----------\n",
      "train Loss: 0.5255 Acc: 0.7336\n",
      "has spend time 67m 57s/n\n",
      "val Loss: 0.5591 Acc: 0.6993\n",
      "has spend time 67m 57s/n\n",
      "\n",
      "Epoch 1910/9999\n",
      "----------\n",
      "train Loss: 0.4914 Acc: 0.7500\n",
      "has spend time 67m 59s/n\n",
      "val Loss: 0.5440 Acc: 0.7255\n",
      "has spend time 67m 59s/n\n",
      "\n",
      "Epoch 1911/9999\n",
      "----------\n",
      "train Loss: 0.5062 Acc: 0.7049\n",
      "has spend time 68m 1s/n\n",
      "val Loss: 0.5474 Acc: 0.7190\n",
      "has spend time 68m 1s/n\n",
      "\n",
      "Epoch 1912/9999\n",
      "----------\n",
      "train Loss: 0.5217 Acc: 0.7090\n",
      "has spend time 68m 3s/n\n",
      "val Loss: 0.5423 Acc: 0.7124\n",
      "has spend time 68m 3s/n\n",
      "\n",
      "Epoch 1913/9999\n",
      "----------\n",
      "train Loss: 0.4855 Acc: 0.7336\n",
      "has spend time 68m 5s/n\n",
      "val Loss: 0.5382 Acc: 0.7124\n",
      "has spend time 68m 5s/n\n",
      "\n",
      "Epoch 1914/9999\n",
      "----------\n",
      "train Loss: 0.4894 Acc: 0.7623\n",
      "has spend time 68m 7s/n\n",
      "val Loss: 0.5487 Acc: 0.7190\n",
      "has spend time 68m 7s/n\n",
      "\n",
      "Epoch 1915/9999\n",
      "----------\n",
      "train Loss: 0.5363 Acc: 0.7049\n",
      "has spend time 68m 9s/n\n",
      "val Loss: 0.5513 Acc: 0.7059\n",
      "has spend time 68m 10s/n\n",
      "\n",
      "Epoch 1916/9999\n",
      "----------\n",
      "train Loss: 0.5141 Acc: 0.7377\n",
      "has spend time 68m 11s/n\n",
      "val Loss: 0.5437 Acc: 0.7190\n",
      "has spend time 68m 12s/n\n",
      "\n",
      "Epoch 1917/9999\n",
      "----------\n",
      "train Loss: 0.5007 Acc: 0.7131\n",
      "has spend time 68m 13s/n\n",
      "val Loss: 0.5526 Acc: 0.6928\n",
      "has spend time 68m 14s/n\n",
      "\n",
      "Epoch 1918/9999\n",
      "----------\n",
      "train Loss: 0.5166 Acc: 0.7172\n",
      "has spend time 68m 15s/n\n",
      "val Loss: 0.5501 Acc: 0.7255\n",
      "has spend time 68m 16s/n\n",
      "\n",
      "Epoch 1919/9999\n",
      "----------\n",
      "train Loss: 0.4927 Acc: 0.7418\n",
      "has spend time 68m 17s/n\n",
      "val Loss: 0.5523 Acc: 0.7124\n",
      "has spend time 68m 18s/n\n",
      "\n",
      "Epoch 1920/9999\n",
      "----------\n",
      "train Loss: 0.5196 Acc: 0.7254\n",
      "has spend time 68m 20s/n\n",
      "val Loss: 0.5491 Acc: 0.7059\n",
      "has spend time 68m 20s/n\n",
      "\n",
      "Epoch 1921/9999\n",
      "----------\n",
      "train Loss: 0.5119 Acc: 0.7664\n",
      "has spend time 68m 22s/n\n",
      "val Loss: 0.5513 Acc: 0.7190\n",
      "has spend time 68m 23s/n\n",
      "\n",
      "Epoch 1922/9999\n",
      "----------\n",
      "train Loss: 0.5071 Acc: 0.7664\n",
      "has spend time 68m 24s/n\n",
      "val Loss: 0.5617 Acc: 0.6993\n",
      "has spend time 68m 25s/n\n",
      "\n",
      "Epoch 1923/9999\n",
      "----------\n",
      "train Loss: 0.5269 Acc: 0.7213\n",
      "has spend time 68m 26s/n\n",
      "val Loss: 0.5488 Acc: 0.7059\n",
      "has spend time 68m 27s/n\n",
      "\n",
      "Epoch 1924/9999\n",
      "----------\n",
      "train Loss: 0.4819 Acc: 0.7459\n",
      "has spend time 68m 28s/n\n",
      "val Loss: 0.5597 Acc: 0.6993\n",
      "has spend time 68m 29s/n\n",
      "\n",
      "Epoch 1925/9999\n",
      "----------\n",
      "train Loss: 0.4734 Acc: 0.7869\n",
      "has spend time 68m 30s/n\n",
      "val Loss: 0.5504 Acc: 0.7059\n",
      "has spend time 68m 31s/n\n",
      "\n",
      "Epoch 1926/9999\n",
      "----------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train Loss: 0.4951 Acc: 0.7295\n",
      "has spend time 68m 33s/n\n",
      "val Loss: 0.5532 Acc: 0.6993\n",
      "has spend time 68m 33s/n\n",
      "\n",
      "Epoch 1927/9999\n",
      "----------\n",
      "train Loss: 0.4834 Acc: 0.7869\n",
      "has spend time 68m 35s/n\n",
      "val Loss: 0.5522 Acc: 0.7124\n",
      "has spend time 68m 35s/n\n",
      "\n",
      "Epoch 1928/9999\n",
      "----------\n",
      "train Loss: 0.5171 Acc: 0.7049\n",
      "has spend time 68m 37s/n\n",
      "val Loss: 0.5485 Acc: 0.7124\n",
      "has spend time 68m 37s/n\n",
      "\n",
      "Epoch 1929/9999\n",
      "----------\n",
      "train Loss: 0.5266 Acc: 0.7090\n",
      "has spend time 68m 39s/n\n",
      "val Loss: 0.5528 Acc: 0.7059\n",
      "has spend time 68m 39s/n\n",
      "\n",
      "Epoch 1930/9999\n",
      "----------\n",
      "train Loss: 0.4880 Acc: 0.7500\n",
      "has spend time 68m 41s/n\n",
      "val Loss: 0.5393 Acc: 0.7190\n",
      "has spend time 68m 41s/n\n",
      "\n",
      "Epoch 1931/9999\n",
      "----------\n",
      "train Loss: 0.5008 Acc: 0.7336\n",
      "has spend time 68m 43s/n\n",
      "val Loss: 0.5494 Acc: 0.7059\n",
      "has spend time 68m 44s/n\n",
      "\n",
      "Epoch 1932/9999\n",
      "----------\n",
      "train Loss: 0.5025 Acc: 0.7336\n",
      "has spend time 68m 45s/n\n",
      "val Loss: 0.5433 Acc: 0.7124\n",
      "has spend time 68m 46s/n\n",
      "\n",
      "Epoch 1933/9999\n",
      "----------\n",
      "train Loss: 0.4875 Acc: 0.7582\n",
      "has spend time 68m 48s/n\n",
      "val Loss: 0.5565 Acc: 0.6993\n",
      "has spend time 68m 49s/n\n",
      "\n",
      "Epoch 1934/9999\n",
      "----------\n",
      "train Loss: 0.5171 Acc: 0.7418\n",
      "has spend time 68m 50s/n\n",
      "val Loss: 0.5660 Acc: 0.7059\n",
      "has spend time 68m 51s/n\n",
      "\n",
      "Epoch 1935/9999\n",
      "----------\n",
      "train Loss: 0.5378 Acc: 0.7131\n",
      "has spend time 68m 52s/n\n",
      "val Loss: 0.5673 Acc: 0.6993\n",
      "has spend time 68m 53s/n\n",
      "\n",
      "Epoch 1936/9999\n",
      "----------\n",
      "train Loss: 0.4888 Acc: 0.7623\n",
      "has spend time 68m 55s/n\n",
      "val Loss: 0.5412 Acc: 0.7124\n",
      "has spend time 68m 55s/n\n",
      "\n",
      "Epoch 1937/9999\n",
      "----------\n",
      "train Loss: 0.5069 Acc: 0.7336\n",
      "has spend time 68m 57s/n\n",
      "val Loss: 0.5698 Acc: 0.6993\n",
      "has spend time 68m 57s/n\n",
      "\n",
      "Epoch 1938/9999\n",
      "----------\n",
      "train Loss: 0.5203 Acc: 0.7418\n",
      "has spend time 68m 59s/n\n",
      "val Loss: 0.5473 Acc: 0.7190\n",
      "has spend time 68m 59s/n\n",
      "\n",
      "Epoch 1939/9999\n",
      "----------\n",
      "train Loss: 0.5612 Acc: 0.7131\n",
      "has spend time 69m 1s/n\n",
      "val Loss: 0.5519 Acc: 0.7124\n",
      "has spend time 69m 1s/n\n",
      "\n",
      "Epoch 1940/9999\n",
      "----------\n",
      "train Loss: 0.5255 Acc: 0.7131\n",
      "has spend time 69m 3s/n\n",
      "val Loss: 0.5429 Acc: 0.7190\n",
      "has spend time 69m 3s/n\n",
      "\n",
      "Epoch 1941/9999\n",
      "----------\n",
      "train Loss: 0.5009 Acc: 0.7500\n",
      "has spend time 69m 5s/n\n",
      "val Loss: 0.5488 Acc: 0.7124\n",
      "has spend time 69m 6s/n\n",
      "\n",
      "Epoch 1942/9999\n",
      "----------\n",
      "train Loss: 0.5069 Acc: 0.7377\n",
      "has spend time 69m 7s/n\n",
      "val Loss: 0.5516 Acc: 0.7059\n",
      "has spend time 69m 8s/n\n",
      "\n",
      "Epoch 1943/9999\n",
      "----------\n",
      "train Loss: 0.5177 Acc: 0.7377\n",
      "has spend time 69m 9s/n\n",
      "val Loss: 0.5582 Acc: 0.7059\n",
      "has spend time 69m 10s/n\n",
      "\n",
      "Epoch 1944/9999\n",
      "----------\n",
      "train Loss: 0.4842 Acc: 0.7746\n",
      "has spend time 69m 11s/n\n",
      "val Loss: 0.5463 Acc: 0.7124\n",
      "has spend time 69m 12s/n\n",
      "\n",
      "Epoch 1945/9999\n",
      "----------\n",
      "train Loss: 0.5210 Acc: 0.7295\n",
      "has spend time 69m 13s/n\n",
      "val Loss: 0.5557 Acc: 0.6928\n",
      "has spend time 69m 14s/n\n",
      "\n",
      "Epoch 1946/9999\n",
      "----------\n",
      "train Loss: 0.5158 Acc: 0.7213\n",
      "has spend time 69m 15s/n\n",
      "val Loss: 0.5454 Acc: 0.7124\n",
      "has spend time 69m 16s/n\n",
      "\n",
      "Epoch 1947/9999\n",
      "----------\n",
      "train Loss: 0.4931 Acc: 0.7582\n",
      "has spend time 69m 17s/n\n",
      "val Loss: 0.5435 Acc: 0.7124\n",
      "has spend time 69m 18s/n\n",
      "\n",
      "Epoch 1948/9999\n",
      "----------\n",
      "train Loss: 0.5302 Acc: 0.7049\n",
      "has spend time 69m 20s/n\n",
      "val Loss: 0.5569 Acc: 0.6928\n",
      "has spend time 69m 20s/n\n",
      "\n",
      "Epoch 1949/9999\n",
      "----------\n",
      "train Loss: 0.4927 Acc: 0.7336\n",
      "has spend time 69m 22s/n\n",
      "val Loss: 0.5556 Acc: 0.6993\n",
      "has spend time 69m 23s/n\n",
      "\n",
      "Epoch 1950/9999\n",
      "----------\n",
      "train Loss: 0.5284 Acc: 0.6967\n",
      "has spend time 69m 24s/n\n",
      "val Loss: 0.5603 Acc: 0.6993\n",
      "has spend time 69m 25s/n\n",
      "\n",
      "Epoch 1951/9999\n",
      "----------\n",
      "train Loss: 0.5261 Acc: 0.7418\n",
      "has spend time 69m 26s/n\n",
      "val Loss: 0.5547 Acc: 0.7190\n",
      "has spend time 69m 27s/n\n",
      "\n",
      "Epoch 1952/9999\n",
      "----------\n",
      "train Loss: 0.4992 Acc: 0.7213\n",
      "has spend time 69m 28s/n\n",
      "val Loss: 0.5560 Acc: 0.6928\n",
      "has spend time 69m 29s/n\n",
      "\n",
      "Epoch 1953/9999\n",
      "----------\n",
      "train Loss: 0.4855 Acc: 0.7787\n",
      "has spend time 69m 30s/n\n",
      "val Loss: 0.5450 Acc: 0.7255\n",
      "has spend time 69m 31s/n\n",
      "\n",
      "Epoch 1954/9999\n",
      "----------\n",
      "train Loss: 0.4969 Acc: 0.7418\n",
      "has spend time 69m 33s/n\n",
      "val Loss: 0.5508 Acc: 0.7124\n",
      "has spend time 69m 33s/n\n",
      "\n",
      "Epoch 1955/9999\n",
      "----------\n",
      "train Loss: 0.5086 Acc: 0.7336\n",
      "has spend time 69m 35s/n\n",
      "val Loss: 0.5558 Acc: 0.7190\n",
      "has spend time 69m 36s/n\n",
      "\n",
      "Epoch 1956/9999\n",
      "----------\n",
      "train Loss: 0.5251 Acc: 0.7131\n",
      "has spend time 69m 37s/n\n",
      "val Loss: 0.5665 Acc: 0.6928\n",
      "has spend time 69m 38s/n\n",
      "\n",
      "Epoch 1957/9999\n",
      "----------\n",
      "train Loss: 0.5189 Acc: 0.7254\n",
      "has spend time 69m 39s/n\n",
      "val Loss: 0.5489 Acc: 0.7059\n",
      "has spend time 69m 40s/n\n",
      "\n",
      "Epoch 1958/9999\n",
      "----------\n",
      "train Loss: 0.5064 Acc: 0.7582\n",
      "has spend time 69m 41s/n\n",
      "val Loss: 0.5491 Acc: 0.7124\n",
      "has spend time 69m 42s/n\n",
      "\n",
      "Epoch 1959/9999\n",
      "----------\n",
      "train Loss: 0.5307 Acc: 0.6762\n",
      "has spend time 69m 43s/n\n",
      "val Loss: 0.5593 Acc: 0.6993\n",
      "has spend time 69m 44s/n\n",
      "\n",
      "Epoch 1960/9999\n",
      "----------\n",
      "train Loss: 0.5424 Acc: 0.7787\n",
      "has spend time 69m 45s/n\n",
      "val Loss: 0.5511 Acc: 0.6993\n",
      "has spend time 69m 46s/n\n",
      "\n",
      "Epoch 1961/9999\n",
      "----------\n",
      "train Loss: 0.4987 Acc: 0.7500\n",
      "has spend time 69m 47s/n\n",
      "val Loss: 0.5448 Acc: 0.7124\n",
      "has spend time 69m 48s/n\n",
      "\n",
      "Epoch 1962/9999\n",
      "----------\n",
      "train Loss: 0.4907 Acc: 0.7295\n",
      "has spend time 69m 49s/n\n",
      "val Loss: 0.5539 Acc: 0.7059\n",
      "has spend time 69m 50s/n\n",
      "\n",
      "Epoch 1963/9999\n",
      "----------\n",
      "train Loss: 0.5335 Acc: 0.6967\n",
      "has spend time 69m 51s/n\n",
      "val Loss: 0.5487 Acc: 0.7059\n",
      "has spend time 69m 52s/n\n",
      "\n",
      "Epoch 1964/9999\n",
      "----------\n",
      "train Loss: 0.4880 Acc: 0.7459\n",
      "has spend time 69m 54s/n\n",
      "val Loss: 0.5453 Acc: 0.7124\n",
      "has spend time 69m 54s/n\n",
      "\n",
      "Epoch 1965/9999\n",
      "----------\n",
      "train Loss: 0.5063 Acc: 0.7459\n",
      "has spend time 69m 56s/n\n",
      "val Loss: 0.5457 Acc: 0.7124\n",
      "has spend time 69m 56s/n\n",
      "\n",
      "Epoch 1966/9999\n",
      "----------\n",
      "train Loss: 0.4732 Acc: 0.7459\n",
      "has spend time 69m 58s/n\n",
      "val Loss: 0.5641 Acc: 0.6993\n",
      "has spend time 69m 58s/n\n",
      "\n",
      "Epoch 1967/9999\n",
      "----------\n",
      "train Loss: 0.5029 Acc: 0.7541\n",
      "has spend time 69m 60s/n\n",
      "val Loss: 0.5559 Acc: 0.6993\n",
      "has spend time 70m 0s/n\n",
      "\n",
      "Epoch 1968/9999\n",
      "----------\n",
      "train Loss: 0.4907 Acc: 0.7254\n",
      "has spend time 70m 2s/n\n",
      "val Loss: 0.5748 Acc: 0.6863\n",
      "has spend time 70m 3s/n\n",
      "\n",
      "Epoch 1969/9999\n",
      "----------\n",
      "train Loss: 0.4966 Acc: 0.7746\n",
      "has spend time 70m 4s/n\n",
      "val Loss: 0.5595 Acc: 0.6928\n",
      "has spend time 70m 5s/n\n",
      "\n",
      "Epoch 1970/9999\n",
      "----------\n",
      "train Loss: 0.5039 Acc: 0.7295\n",
      "has spend time 70m 7s/n\n",
      "val Loss: 0.5601 Acc: 0.6993\n",
      "has spend time 70m 7s/n\n",
      "\n",
      "Epoch 1971/9999\n",
      "----------\n",
      "train Loss: 0.5190 Acc: 0.7377\n",
      "has spend time 70m 9s/n\n",
      "val Loss: 0.5497 Acc: 0.7190\n",
      "has spend time 70m 9s/n\n",
      "\n",
      "Epoch 1972/9999\n",
      "----------\n",
      "train Loss: 0.4995 Acc: 0.7336\n",
      "has spend time 70m 11s/n\n",
      "val Loss: 0.5679 Acc: 0.6797\n",
      "has spend time 70m 11s/n\n",
      "\n",
      "Epoch 1973/9999\n",
      "----------\n",
      "train Loss: 0.4918 Acc: 0.7336\n",
      "has spend time 70m 13s/n\n",
      "val Loss: 0.5528 Acc: 0.6993\n",
      "has spend time 70m 14s/n\n",
      "\n",
      "Epoch 1974/9999\n",
      "----------\n",
      "train Loss: 0.4849 Acc: 0.7459\n",
      "has spend time 70m 15s/n\n",
      "val Loss: 0.5416 Acc: 0.7190\n",
      "has spend time 70m 16s/n\n",
      "\n",
      "Epoch 1975/9999\n",
      "----------\n",
      "train Loss: 0.5162 Acc: 0.7254\n",
      "has spend time 70m 17s/n\n",
      "val Loss: 0.5440 Acc: 0.7190\n",
      "has spend time 70m 18s/n\n",
      "\n",
      "Epoch 1976/9999\n",
      "----------\n",
      "train Loss: 0.5176 Acc: 0.7418\n",
      "has spend time 70m 19s/n\n",
      "val Loss: 0.5474 Acc: 0.6993\n",
      "has spend time 70m 20s/n\n",
      "\n",
      "Epoch 1977/9999\n",
      "----------\n",
      "train Loss: 0.5200 Acc: 0.7213\n",
      "has spend time 70m 21s/n\n",
      "val Loss: 0.5458 Acc: 0.7059\n",
      "has spend time 70m 22s/n\n",
      "\n",
      "Epoch 1978/9999\n",
      "----------\n",
      "train Loss: 0.5058 Acc: 0.7541\n",
      "has spend time 70m 23s/n\n",
      "val Loss: 0.5524 Acc: 0.7124\n",
      "has spend time 70m 24s/n\n",
      "\n",
      "Epoch 1979/9999\n",
      "----------\n",
      "train Loss: 0.5555 Acc: 0.7131\n",
      "has spend time 70m 26s/n\n",
      "val Loss: 0.5489 Acc: 0.7190\n",
      "has spend time 70m 27s/n\n",
      "\n",
      "Epoch 1980/9999\n",
      "----------\n",
      "train Loss: 0.4989 Acc: 0.7500\n",
      "has spend time 70m 28s/n\n",
      "val Loss: 0.5463 Acc: 0.7124\n",
      "has spend time 70m 29s/n\n",
      "\n",
      "Epoch 1981/9999\n",
      "----------\n",
      "train Loss: 0.5050 Acc: 0.7377\n",
      "has spend time 70m 30s/n\n",
      "val Loss: 0.5469 Acc: 0.7124\n",
      "has spend time 70m 31s/n\n",
      "\n",
      "Epoch 1982/9999\n",
      "----------\n",
      "train Loss: 0.5081 Acc: 0.7500\n",
      "has spend time 70m 32s/n\n",
      "val Loss: 0.5588 Acc: 0.7124\n",
      "has spend time 70m 33s/n\n",
      "\n",
      "Epoch 1983/9999\n",
      "----------\n",
      "train Loss: 0.4754 Acc: 0.7541\n",
      "has spend time 70m 34s/n\n",
      "val Loss: 0.5489 Acc: 0.7059\n",
      "has spend time 70m 35s/n\n",
      "\n",
      "Epoch 1984/9999\n",
      "----------\n",
      "train Loss: 0.4967 Acc: 0.7336\n",
      "has spend time 70m 36s/n\n",
      "val Loss: 0.5532 Acc: 0.7190\n",
      "has spend time 70m 37s/n\n",
      "\n",
      "Epoch 1985/9999\n",
      "----------\n",
      "train Loss: 0.5001 Acc: 0.7295\n",
      "has spend time 70m 39s/n\n",
      "val Loss: 0.5566 Acc: 0.6928\n",
      "has spend time 70m 39s/n\n",
      "\n",
      "Epoch 1986/9999\n",
      "----------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train Loss: 0.4909 Acc: 0.7582\n",
      "has spend time 70m 41s/n\n",
      "val Loss: 0.5596 Acc: 0.7124\n",
      "has spend time 70m 41s/n\n",
      "\n",
      "Epoch 1987/9999\n",
      "----------\n",
      "train Loss: 0.5376 Acc: 0.7213\n",
      "has spend time 70m 43s/n\n",
      "val Loss: 0.5503 Acc: 0.7124\n",
      "has spend time 70m 43s/n\n",
      "\n",
      "Epoch 1988/9999\n",
      "----------\n",
      "train Loss: 0.4781 Acc: 0.7623\n",
      "has spend time 70m 45s/n\n",
      "val Loss: 0.5493 Acc: 0.7059\n",
      "has spend time 70m 45s/n\n",
      "\n",
      "Epoch 1989/9999\n",
      "----------\n",
      "train Loss: 0.4888 Acc: 0.7500\n",
      "has spend time 70m 47s/n\n",
      "val Loss: 0.5456 Acc: 0.7190\n",
      "has spend time 70m 47s/n\n",
      "\n",
      "Epoch 1990/9999\n",
      "----------\n",
      "train Loss: 0.5389 Acc: 0.6926\n",
      "has spend time 70m 49s/n\n",
      "val Loss: 0.5435 Acc: 0.7190\n",
      "has spend time 70m 49s/n\n",
      "\n",
      "Epoch 1991/9999\n",
      "----------\n",
      "train Loss: 0.5207 Acc: 0.7131\n",
      "has spend time 70m 51s/n\n",
      "val Loss: 0.5610 Acc: 0.6928\n",
      "has spend time 70m 52s/n\n",
      "\n",
      "Epoch 1992/9999\n",
      "----------\n",
      "train Loss: 0.4986 Acc: 0.7664\n",
      "has spend time 70m 53s/n\n",
      "val Loss: 0.5428 Acc: 0.7124\n",
      "has spend time 70m 54s/n\n",
      "\n",
      "Epoch 1993/9999\n",
      "----------\n",
      "train Loss: 0.4891 Acc: 0.7500\n",
      "has spend time 70m 55s/n\n",
      "val Loss: 0.5446 Acc: 0.7190\n",
      "has spend time 70m 56s/n\n",
      "\n",
      "Epoch 1994/9999\n",
      "----------\n",
      "train Loss: 0.5180 Acc: 0.7377\n",
      "has spend time 70m 58s/n\n",
      "val Loss: 0.5507 Acc: 0.7059\n",
      "has spend time 70m 58s/n\n",
      "\n",
      "Epoch 1995/9999\n",
      "----------\n",
      "train Loss: 0.4941 Acc: 0.7377\n",
      "has spend time 70m 60s/n\n",
      "val Loss: 0.5504 Acc: 0.7059\n",
      "has spend time 71m 0s/n\n",
      "\n",
      "Epoch 1996/9999\n",
      "----------\n",
      "train Loss: 0.5369 Acc: 0.7295\n",
      "has spend time 71m 2s/n\n",
      "val Loss: 0.5484 Acc: 0.7190\n",
      "has spend time 71m 2s/n\n",
      "\n",
      "Epoch 1997/9999\n",
      "----------\n",
      "train Loss: 0.5005 Acc: 0.7418\n",
      "has spend time 71m 4s/n\n",
      "val Loss: 0.5426 Acc: 0.7255\n",
      "has spend time 71m 4s/n\n",
      "\n",
      "Epoch 1998/9999\n",
      "----------\n",
      "train Loss: 0.5140 Acc: 0.7500\n",
      "has spend time 71m 6s/n\n",
      "val Loss: 0.5645 Acc: 0.6928\n",
      "has spend time 71m 7s/n\n",
      "\n",
      "Epoch 1999/9999\n",
      "----------\n",
      "train Loss: 0.5056 Acc: 0.7213\n",
      "has spend time 71m 8s/n\n",
      "val Loss: 0.5546 Acc: 0.6928\n",
      "has spend time 71m 9s/n\n",
      "\n",
      "Epoch 2000/9999\n",
      "----------\n",
      "train Loss: 0.4898 Acc: 0.7705\n",
      "has spend time 71m 10s/n\n",
      "val Loss: 0.5436 Acc: 0.7059\n",
      "has spend time 71m 11s/n\n",
      "\n",
      "Epoch 2001/9999\n",
      "----------\n",
      "train Loss: 0.4861 Acc: 0.7705\n",
      "has spend time 71m 13s/n\n",
      "val Loss: 0.5598 Acc: 0.6928\n",
      "has spend time 71m 13s/n\n",
      "\n",
      "Epoch 2002/9999\n",
      "----------\n",
      "train Loss: 0.5206 Acc: 0.7336\n",
      "has spend time 71m 15s/n\n",
      "val Loss: 0.5446 Acc: 0.7124\n",
      "has spend time 71m 15s/n\n",
      "\n",
      "Epoch 2003/9999\n",
      "----------\n",
      "train Loss: 0.5036 Acc: 0.7418\n",
      "has spend time 71m 17s/n\n",
      "val Loss: 0.5546 Acc: 0.7190\n",
      "has spend time 71m 17s/n\n",
      "\n",
      "Epoch 2004/9999\n",
      "----------\n",
      "train Loss: 0.4941 Acc: 0.7664\n",
      "has spend time 71m 19s/n\n",
      "val Loss: 0.5540 Acc: 0.7190\n",
      "has spend time 71m 20s/n\n",
      "\n",
      "Epoch 2005/9999\n",
      "----------\n",
      "train Loss: 0.5044 Acc: 0.7418\n",
      "has spend time 71m 21s/n\n",
      "val Loss: 0.5443 Acc: 0.7255\n",
      "has spend time 71m 22s/n\n",
      "\n",
      "Epoch 2006/9999\n",
      "----------\n",
      "train Loss: 0.5319 Acc: 0.7377\n",
      "has spend time 71m 23s/n\n",
      "val Loss: 0.5504 Acc: 0.7255\n",
      "has spend time 71m 24s/n\n",
      "\n",
      "Epoch 2007/9999\n",
      "----------\n",
      "train Loss: 0.5090 Acc: 0.7418\n",
      "has spend time 71m 25s/n\n",
      "val Loss: 0.5571 Acc: 0.7124\n",
      "has spend time 71m 26s/n\n",
      "\n",
      "Epoch 2008/9999\n",
      "----------\n",
      "train Loss: 0.4808 Acc: 0.7787\n",
      "has spend time 71m 27s/n\n",
      "val Loss: 0.5564 Acc: 0.7059\n",
      "has spend time 71m 28s/n\n",
      "\n",
      "Epoch 2009/9999\n",
      "----------\n",
      "train Loss: 0.4837 Acc: 0.7951\n",
      "has spend time 71m 30s/n\n",
      "val Loss: 0.5485 Acc: 0.6928\n",
      "has spend time 71m 30s/n\n",
      "\n",
      "Epoch 2010/9999\n",
      "----------\n",
      "train Loss: 0.5181 Acc: 0.6926\n",
      "has spend time 71m 32s/n\n",
      "val Loss: 0.5446 Acc: 0.7124\n",
      "has spend time 71m 32s/n\n",
      "\n",
      "Epoch 2011/9999\n",
      "----------\n",
      "train Loss: 0.5178 Acc: 0.7664\n",
      "has spend time 71m 34s/n\n",
      "val Loss: 0.5505 Acc: 0.7190\n",
      "has spend time 71m 34s/n\n",
      "\n",
      "Epoch 2012/9999\n",
      "----------\n",
      "train Loss: 0.5300 Acc: 0.7008\n",
      "has spend time 71m 36s/n\n",
      "val Loss: 0.5486 Acc: 0.7190\n",
      "has spend time 71m 36s/n\n",
      "\n",
      "Epoch 2013/9999\n",
      "----------\n",
      "train Loss: 0.4745 Acc: 0.7582\n",
      "has spend time 71m 38s/n\n",
      "val Loss: 0.5478 Acc: 0.7190\n",
      "has spend time 71m 38s/n\n",
      "\n",
      "Epoch 2014/9999\n",
      "----------\n",
      "train Loss: 0.5406 Acc: 0.7131\n",
      "has spend time 71m 40s/n\n",
      "val Loss: 0.5468 Acc: 0.7124\n",
      "has spend time 71m 41s/n\n",
      "\n",
      "Epoch 2015/9999\n",
      "----------\n",
      "train Loss: 0.5875 Acc: 0.6926\n",
      "has spend time 71m 42s/n\n",
      "val Loss: 0.5662 Acc: 0.6863\n",
      "has spend time 71m 43s/n\n",
      "\n",
      "Epoch 2016/9999\n",
      "----------\n",
      "train Loss: 0.4698 Acc: 0.7787\n",
      "has spend time 71m 44s/n\n",
      "val Loss: 0.5516 Acc: 0.7190\n",
      "has spend time 71m 45s/n\n",
      "\n",
      "Epoch 2017/9999\n",
      "----------\n",
      "train Loss: 0.4908 Acc: 0.7418\n",
      "has spend time 71m 46s/n\n",
      "val Loss: 0.5459 Acc: 0.7255\n",
      "has spend time 71m 47s/n\n",
      "\n",
      "Epoch 2018/9999\n",
      "----------\n",
      "train Loss: 0.5133 Acc: 0.7541\n",
      "has spend time 71m 48s/n\n",
      "val Loss: 0.5439 Acc: 0.7255\n",
      "has spend time 71m 49s/n\n",
      "\n",
      "Epoch 2019/9999\n",
      "----------\n",
      "train Loss: 0.4944 Acc: 0.7418\n",
      "has spend time 71m 50s/n\n",
      "val Loss: 0.5403 Acc: 0.7124\n",
      "has spend time 71m 51s/n\n",
      "\n",
      "Epoch 2020/9999\n",
      "----------\n",
      "train Loss: 0.5076 Acc: 0.7418\n",
      "has spend time 71m 53s/n\n",
      "val Loss: 0.5455 Acc: 0.7124\n",
      "has spend time 71m 53s/n\n",
      "\n",
      "Epoch 2021/9999\n",
      "----------\n",
      "train Loss: 0.5008 Acc: 0.7623\n",
      "has spend time 71m 55s/n\n",
      "val Loss: 0.5501 Acc: 0.7190\n",
      "has spend time 71m 56s/n\n",
      "\n",
      "Epoch 2022/9999\n",
      "----------\n",
      "train Loss: 0.5483 Acc: 0.7172\n",
      "has spend time 71m 57s/n\n",
      "val Loss: 0.5502 Acc: 0.7124\n",
      "has spend time 71m 58s/n\n",
      "\n",
      "Epoch 2023/9999\n",
      "----------\n",
      "train Loss: 0.5064 Acc: 0.7459\n",
      "has spend time 71m 59s/n\n",
      "val Loss: 0.5558 Acc: 0.6993\n",
      "has spend time 71m 60s/n\n",
      "\n",
      "Epoch 2024/9999\n",
      "----------\n",
      "train Loss: 0.5598 Acc: 0.7008\n",
      "has spend time 72m 1s/n\n",
      "val Loss: 0.5593 Acc: 0.7124\n",
      "has spend time 72m 2s/n\n",
      "\n",
      "Epoch 2025/9999\n",
      "----------\n",
      "train Loss: 0.4966 Acc: 0.7582\n",
      "has spend time 72m 3s/n\n",
      "val Loss: 0.5573 Acc: 0.7059\n",
      "has spend time 72m 4s/n\n",
      "\n",
      "Epoch 2026/9999\n",
      "----------\n",
      "train Loss: 0.4862 Acc: 0.7787\n",
      "has spend time 72m 5s/n\n",
      "val Loss: 0.5469 Acc: 0.7124\n",
      "has spend time 72m 6s/n\n",
      "\n",
      "Epoch 2027/9999\n",
      "----------\n",
      "train Loss: 0.4801 Acc: 0.7459\n",
      "has spend time 72m 7s/n\n",
      "val Loss: 0.5514 Acc: 0.7124\n",
      "has spend time 72m 8s/n\n",
      "\n",
      "Epoch 2028/9999\n",
      "----------\n",
      "train Loss: 0.5220 Acc: 0.7336\n",
      "has spend time 72m 10s/n\n",
      "val Loss: 0.5662 Acc: 0.6928\n",
      "has spend time 72m 10s/n\n",
      "\n",
      "Epoch 2029/9999\n",
      "----------\n",
      "train Loss: 0.5184 Acc: 0.7418\n",
      "has spend time 72m 12s/n\n",
      "val Loss: 0.5549 Acc: 0.7124\n",
      "has spend time 72m 12s/n\n",
      "\n",
      "Epoch 2030/9999\n",
      "----------\n",
      "train Loss: 0.5240 Acc: 0.7418\n",
      "has spend time 72m 14s/n\n",
      "val Loss: 0.5566 Acc: 0.6993\n",
      "has spend time 72m 14s/n\n",
      "\n",
      "Epoch 2031/9999\n",
      "----------\n",
      "train Loss: 0.4825 Acc: 0.7705\n",
      "has spend time 72m 16s/n\n",
      "val Loss: 0.5659 Acc: 0.6993\n",
      "has spend time 72m 16s/n\n",
      "\n",
      "Epoch 2032/9999\n",
      "----------\n",
      "train Loss: 0.5003 Acc: 0.7295\n",
      "has spend time 72m 18s/n\n",
      "val Loss: 0.5550 Acc: 0.6993\n",
      "has spend time 72m 18s/n\n",
      "\n",
      "Epoch 2033/9999\n",
      "----------\n",
      "train Loss: 0.5044 Acc: 0.7459\n",
      "has spend time 72m 20s/n\n",
      "val Loss: 0.5663 Acc: 0.6928\n",
      "has spend time 72m 20s/n\n",
      "\n",
      "Epoch 2034/9999\n",
      "----------\n",
      "train Loss: 0.5357 Acc: 0.7131\n",
      "has spend time 72m 22s/n\n",
      "val Loss: 0.5484 Acc: 0.7124\n",
      "has spend time 72m 22s/n\n",
      "\n",
      "Epoch 2035/9999\n",
      "----------\n",
      "train Loss: 0.5092 Acc: 0.7172\n",
      "has spend time 72m 24s/n\n",
      "val Loss: 0.5498 Acc: 0.7059\n",
      "has spend time 72m 24s/n\n",
      "\n",
      "Epoch 2036/9999\n",
      "----------\n",
      "train Loss: 0.4829 Acc: 0.7623\n",
      "has spend time 72m 26s/n\n",
      "val Loss: 0.5443 Acc: 0.7059\n",
      "has spend time 72m 27s/n\n",
      "\n",
      "Epoch 2037/9999\n",
      "----------\n",
      "train Loss: 0.5466 Acc: 0.7049\n",
      "has spend time 72m 28s/n\n",
      "val Loss: 0.5464 Acc: 0.7190\n",
      "has spend time 72m 29s/n\n",
      "\n",
      "Epoch 2038/9999\n",
      "----------\n",
      "train Loss: 0.4938 Acc: 0.7418\n",
      "has spend time 72m 31s/n\n",
      "val Loss: 0.5438 Acc: 0.7059\n",
      "has spend time 72m 31s/n\n",
      "\n",
      "Epoch 2039/9999\n",
      "----------\n",
      "train Loss: 0.4826 Acc: 0.7336\n",
      "has spend time 72m 33s/n\n",
      "val Loss: 0.5501 Acc: 0.6993\n",
      "has spend time 72m 33s/n\n",
      "\n",
      "Epoch 2040/9999\n",
      "----------\n",
      "train Loss: 0.4859 Acc: 0.7664\n",
      "has spend time 72m 35s/n\n",
      "val Loss: 0.5467 Acc: 0.6993\n",
      "has spend time 72m 35s/n\n",
      "\n",
      "Epoch 2041/9999\n",
      "----------\n",
      "train Loss: 0.5152 Acc: 0.7254\n",
      "has spend time 72m 37s/n\n",
      "val Loss: 0.5529 Acc: 0.7059\n",
      "has spend time 72m 38s/n\n",
      "\n",
      "Epoch 2042/9999\n",
      "----------\n",
      "train Loss: 0.4941 Acc: 0.7582\n",
      "has spend time 72m 39s/n\n",
      "val Loss: 0.5585 Acc: 0.7059\n",
      "has spend time 72m 40s/n\n",
      "\n",
      "Epoch 2043/9999\n",
      "----------\n",
      "train Loss: 0.5425 Acc: 0.6967\n",
      "has spend time 72m 41s/n\n",
      "val Loss: 0.5684 Acc: 0.6993\n",
      "has spend time 72m 42s/n\n",
      "\n",
      "Epoch 2044/9999\n",
      "----------\n",
      "train Loss: 0.5067 Acc: 0.7377\n",
      "has spend time 72m 43s/n\n",
      "val Loss: 0.5575 Acc: 0.6993\n",
      "has spend time 72m 44s/n\n",
      "\n",
      "Epoch 2045/9999\n",
      "----------\n",
      "train Loss: 0.5103 Acc: 0.7254\n",
      "has spend time 72m 45s/n\n",
      "val Loss: 0.5517 Acc: 0.7124\n",
      "has spend time 72m 46s/n\n",
      "\n",
      "Epoch 2046/9999\n",
      "----------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train Loss: 0.5328 Acc: 0.7418\n",
      "has spend time 72m 48s/n\n",
      "val Loss: 0.5409 Acc: 0.7124\n",
      "has spend time 72m 48s/n\n",
      "\n",
      "Epoch 2047/9999\n",
      "----------\n",
      "train Loss: 0.5595 Acc: 0.6680\n",
      "has spend time 72m 50s/n\n",
      "val Loss: 0.5365 Acc: 0.7255\n",
      "has spend time 72m 50s/n\n",
      "\n",
      "Epoch 2048/9999\n",
      "----------\n",
      "train Loss: 0.4983 Acc: 0.7705\n",
      "has spend time 72m 52s/n\n",
      "val Loss: 0.5491 Acc: 0.7059\n",
      "has spend time 72m 52s/n\n",
      "\n",
      "Epoch 2049/9999\n",
      "----------\n",
      "train Loss: 0.5269 Acc: 0.7090\n",
      "has spend time 72m 54s/n\n",
      "val Loss: 0.5597 Acc: 0.6797\n",
      "has spend time 72m 54s/n\n",
      "\n",
      "Epoch 2050/9999\n",
      "----------\n",
      "train Loss: 0.5213 Acc: 0.7500\n",
      "has spend time 72m 56s/n\n",
      "val Loss: 0.5611 Acc: 0.6928\n",
      "has spend time 72m 56s/n\n",
      "\n",
      "Epoch 2051/9999\n",
      "----------\n",
      "train Loss: 0.5155 Acc: 0.7377\n",
      "has spend time 72m 58s/n\n",
      "val Loss: 0.5418 Acc: 0.7059\n",
      "has spend time 72m 58s/n\n",
      "\n",
      "Epoch 2052/9999\n",
      "----------\n",
      "train Loss: 0.4971 Acc: 0.7459\n",
      "has spend time 72m 60s/n\n",
      "val Loss: 0.5711 Acc: 0.6863\n",
      "has spend time 73m 1s/n\n",
      "\n",
      "Epoch 2053/9999\n",
      "----------\n",
      "train Loss: 0.5077 Acc: 0.7377\n",
      "has spend time 73m 2s/n\n",
      "val Loss: 0.5503 Acc: 0.7124\n",
      "has spend time 73m 3s/n\n",
      "\n",
      "Epoch 2054/9999\n",
      "----------\n",
      "train Loss: 0.4806 Acc: 0.7295\n",
      "has spend time 73m 5s/n\n",
      "val Loss: 0.5534 Acc: 0.7124\n",
      "has spend time 73m 5s/n\n",
      "\n",
      "Epoch 2055/9999\n",
      "----------\n",
      "train Loss: 0.4778 Acc: 0.7787\n",
      "has spend time 73m 7s/n\n",
      "val Loss: 0.5481 Acc: 0.6993\n",
      "has spend time 73m 7s/n\n",
      "\n",
      "Epoch 2056/9999\n",
      "----------\n",
      "train Loss: 0.5205 Acc: 0.7500\n",
      "has spend time 73m 9s/n\n",
      "val Loss: 0.5496 Acc: 0.7190\n",
      "has spend time 73m 9s/n\n",
      "\n",
      "Epoch 2057/9999\n",
      "----------\n",
      "train Loss: 0.4906 Acc: 0.7459\n",
      "has spend time 73m 11s/n\n",
      "val Loss: 0.5610 Acc: 0.6993\n",
      "has spend time 73m 11s/n\n",
      "\n",
      "Epoch 2058/9999\n",
      "----------\n",
      "train Loss: 0.4768 Acc: 0.7541\n",
      "has spend time 73m 13s/n\n",
      "val Loss: 0.5472 Acc: 0.7255\n",
      "has spend time 73m 13s/n\n",
      "\n",
      "Epoch 2059/9999\n",
      "----------\n",
      "train Loss: 0.4985 Acc: 0.7459\n",
      "has spend time 73m 15s/n\n",
      "val Loss: 0.5538 Acc: 0.7124\n",
      "has spend time 73m 15s/n\n",
      "\n",
      "Epoch 2060/9999\n",
      "----------\n",
      "train Loss: 0.5089 Acc: 0.7582\n",
      "has spend time 73m 17s/n\n",
      "val Loss: 0.5523 Acc: 0.7059\n",
      "has spend time 73m 18s/n\n",
      "\n",
      "Epoch 2061/9999\n",
      "----------\n",
      "train Loss: 0.4854 Acc: 0.7787\n",
      "has spend time 73m 19s/n\n",
      "val Loss: 0.5524 Acc: 0.7190\n",
      "has spend time 73m 20s/n\n",
      "\n",
      "Epoch 2062/9999\n",
      "----------\n",
      "train Loss: 0.5131 Acc: 0.7131\n",
      "has spend time 73m 21s/n\n",
      "val Loss: 0.5593 Acc: 0.7059\n",
      "has spend time 73m 22s/n\n",
      "\n",
      "Epoch 2063/9999\n",
      "----------\n",
      "train Loss: 0.5139 Acc: 0.7418\n",
      "has spend time 73m 23s/n\n",
      "val Loss: 0.5548 Acc: 0.7059\n",
      "has spend time 73m 24s/n\n",
      "\n",
      "Epoch 2064/9999\n",
      "----------\n",
      "train Loss: 0.4943 Acc: 0.7623\n",
      "has spend time 73m 25s/n\n",
      "val Loss: 0.5492 Acc: 0.7124\n",
      "has spend time 73m 26s/n\n",
      "\n",
      "Epoch 2065/9999\n",
      "----------\n",
      "train Loss: 0.5053 Acc: 0.7131\n",
      "has spend time 73m 27s/n\n",
      "val Loss: 0.5686 Acc: 0.6993\n",
      "has spend time 73m 28s/n\n",
      "\n",
      "Epoch 2066/9999\n",
      "----------\n",
      "train Loss: 0.4931 Acc: 0.7459\n",
      "has spend time 73m 29s/n\n",
      "val Loss: 0.5511 Acc: 0.6993\n",
      "has spend time 73m 30s/n\n",
      "\n",
      "Epoch 2067/9999\n",
      "----------\n",
      "train Loss: 0.4988 Acc: 0.7541\n",
      "has spend time 73m 32s/n\n",
      "val Loss: 0.5519 Acc: 0.7124\n",
      "has spend time 73m 32s/n\n",
      "\n",
      "Epoch 2068/9999\n",
      "----------\n",
      "train Loss: 0.5085 Acc: 0.7254\n",
      "has spend time 73m 34s/n\n",
      "val Loss: 0.5483 Acc: 0.7124\n",
      "has spend time 73m 34s/n\n",
      "\n",
      "Epoch 2069/9999\n",
      "----------\n",
      "train Loss: 0.4719 Acc: 0.7705\n",
      "has spend time 73m 36s/n\n",
      "val Loss: 0.5530 Acc: 0.6928\n",
      "has spend time 73m 36s/n\n",
      "\n",
      "Epoch 2070/9999\n",
      "----------\n",
      "train Loss: 0.5161 Acc: 0.7172\n",
      "has spend time 73m 38s/n\n",
      "val Loss: 0.5499 Acc: 0.7124\n",
      "has spend time 73m 38s/n\n",
      "\n",
      "Epoch 2071/9999\n",
      "----------\n",
      "train Loss: 0.5235 Acc: 0.7049\n",
      "has spend time 73m 40s/n\n",
      "val Loss: 0.5446 Acc: 0.7124\n",
      "has spend time 73m 40s/n\n",
      "\n",
      "Epoch 2072/9999\n",
      "----------\n",
      "train Loss: 0.4971 Acc: 0.7500\n",
      "has spend time 73m 42s/n\n",
      "val Loss: 0.5503 Acc: 0.7190\n",
      "has spend time 73m 43s/n\n",
      "\n",
      "Epoch 2073/9999\n",
      "----------\n",
      "train Loss: 0.5355 Acc: 0.7213\n",
      "has spend time 73m 44s/n\n",
      "val Loss: 0.5438 Acc: 0.7190\n",
      "has spend time 73m 45s/n\n",
      "\n",
      "Epoch 2074/9999\n",
      "----------\n",
      "train Loss: 0.5155 Acc: 0.7131\n",
      "has spend time 73m 46s/n\n",
      "val Loss: 0.5401 Acc: 0.7124\n",
      "has spend time 73m 47s/n\n",
      "\n",
      "Epoch 2075/9999\n",
      "----------\n",
      "train Loss: 0.5109 Acc: 0.6967\n",
      "has spend time 73m 49s/n\n",
      "val Loss: 0.5400 Acc: 0.7124\n",
      "has spend time 73m 49s/n\n",
      "\n",
      "Epoch 2076/9999\n",
      "----------\n",
      "train Loss: 0.5231 Acc: 0.7336\n",
      "has spend time 73m 51s/n\n",
      "val Loss: 0.5470 Acc: 0.7124\n",
      "has spend time 73m 51s/n\n",
      "\n",
      "Epoch 2077/9999\n",
      "----------\n",
      "train Loss: 0.4914 Acc: 0.7377\n",
      "has spend time 73m 53s/n\n",
      "val Loss: 0.5504 Acc: 0.6993\n",
      "has spend time 73m 54s/n\n",
      "\n",
      "Epoch 2078/9999\n",
      "----------\n",
      "train Loss: 0.5454 Acc: 0.7049\n",
      "has spend time 73m 55s/n\n",
      "val Loss: 0.5413 Acc: 0.7255\n",
      "has spend time 73m 56s/n\n",
      "\n",
      "Epoch 2079/9999\n",
      "----------\n",
      "train Loss: 0.5269 Acc: 0.7090\n",
      "has spend time 73m 58s/n\n",
      "val Loss: 0.5400 Acc: 0.7255\n",
      "has spend time 73m 58s/n\n",
      "\n",
      "Epoch 2080/9999\n",
      "----------\n",
      "train Loss: 0.5133 Acc: 0.7295\n",
      "has spend time 73m 60s/n\n",
      "val Loss: 0.5419 Acc: 0.7190\n",
      "has spend time 74m 0s/n\n",
      "\n",
      "Epoch 2081/9999\n",
      "----------\n",
      "train Loss: 0.4807 Acc: 0.7623\n",
      "has spend time 74m 2s/n\n",
      "val Loss: 0.5505 Acc: 0.7124\n",
      "has spend time 74m 2s/n\n",
      "\n",
      "Epoch 2082/9999\n",
      "----------\n",
      "train Loss: 0.5031 Acc: 0.7500\n",
      "has spend time 74m 4s/n\n",
      "val Loss: 0.5500 Acc: 0.6928\n",
      "has spend time 74m 4s/n\n",
      "\n",
      "Epoch 2083/9999\n",
      "----------\n",
      "train Loss: 0.5087 Acc: 0.7377\n",
      "has spend time 74m 6s/n\n",
      "val Loss: 0.5667 Acc: 0.6993\n",
      "has spend time 74m 7s/n\n",
      "\n",
      "Epoch 2084/9999\n",
      "----------\n",
      "train Loss: 0.5212 Acc: 0.6926\n",
      "has spend time 74m 8s/n\n",
      "val Loss: 0.5569 Acc: 0.6797\n",
      "has spend time 74m 9s/n\n",
      "\n",
      "Epoch 2085/9999\n",
      "----------\n",
      "train Loss: 0.5076 Acc: 0.7582\n",
      "has spend time 74m 11s/n\n",
      "val Loss: 0.5448 Acc: 0.6928\n",
      "has spend time 74m 11s/n\n",
      "\n",
      "Epoch 2086/9999\n",
      "----------\n",
      "train Loss: 0.5026 Acc: 0.7582\n",
      "has spend time 74m 13s/n\n",
      "val Loss: 0.5469 Acc: 0.7124\n",
      "has spend time 74m 13s/n\n",
      "\n",
      "Epoch 2087/9999\n",
      "----------\n",
      "train Loss: 0.4874 Acc: 0.7582\n",
      "has spend time 74m 15s/n\n",
      "val Loss: 0.5547 Acc: 0.6993\n",
      "has spend time 74m 15s/n\n",
      "\n",
      "Epoch 2088/9999\n",
      "----------\n",
      "train Loss: 0.5034 Acc: 0.7336\n",
      "has spend time 74m 17s/n\n",
      "val Loss: 0.5528 Acc: 0.7190\n",
      "has spend time 74m 18s/n\n",
      "\n",
      "Epoch 2089/9999\n",
      "----------\n",
      "train Loss: 0.5025 Acc: 0.7377\n",
      "has spend time 74m 19s/n\n",
      "val Loss: 0.5545 Acc: 0.7124\n",
      "has spend time 74m 20s/n\n",
      "\n",
      "Epoch 2090/9999\n",
      "----------\n",
      "train Loss: 0.4992 Acc: 0.7705\n",
      "has spend time 74m 21s/n\n",
      "val Loss: 0.5456 Acc: 0.7190\n",
      "has spend time 74m 22s/n\n",
      "\n",
      "Epoch 2091/9999\n",
      "----------\n",
      "train Loss: 0.5210 Acc: 0.7295\n",
      "has spend time 74m 23s/n\n",
      "val Loss: 0.5475 Acc: 0.7059\n",
      "has spend time 74m 24s/n\n",
      "\n",
      "Epoch 2092/9999\n",
      "----------\n",
      "train Loss: 0.4876 Acc: 0.7623\n",
      "has spend time 74m 25s/n\n",
      "val Loss: 0.5577 Acc: 0.7059\n",
      "has spend time 74m 26s/n\n",
      "\n",
      "Epoch 2093/9999\n",
      "----------\n",
      "train Loss: 0.5248 Acc: 0.7213\n",
      "has spend time 74m 27s/n\n",
      "val Loss: 0.5475 Acc: 0.7124\n",
      "has spend time 74m 28s/n\n",
      "\n",
      "Epoch 2094/9999\n",
      "----------\n",
      "train Loss: 0.5036 Acc: 0.7664\n",
      "has spend time 74m 30s/n\n",
      "val Loss: 0.5439 Acc: 0.7190\n",
      "has spend time 74m 30s/n\n",
      "\n",
      "Epoch 2095/9999\n",
      "----------\n",
      "train Loss: 0.4896 Acc: 0.7541\n",
      "has spend time 74m 32s/n\n",
      "val Loss: 0.5532 Acc: 0.6928\n",
      "has spend time 74m 32s/n\n",
      "\n",
      "Epoch 2096/9999\n",
      "----------\n",
      "train Loss: 0.5279 Acc: 0.7254\n",
      "has spend time 74m 34s/n\n",
      "val Loss: 0.5535 Acc: 0.7124\n",
      "has spend time 74m 34s/n\n",
      "\n",
      "Epoch 2097/9999\n",
      "----------\n",
      "train Loss: 0.5086 Acc: 0.7295\n",
      "has spend time 74m 36s/n\n",
      "val Loss: 0.5493 Acc: 0.6993\n",
      "has spend time 74m 37s/n\n",
      "\n",
      "Epoch 2098/9999\n",
      "----------\n",
      "train Loss: 0.4886 Acc: 0.7418\n",
      "has spend time 74m 38s/n\n",
      "val Loss: 0.5529 Acc: 0.6993\n",
      "has spend time 74m 39s/n\n",
      "\n",
      "Epoch 2099/9999\n",
      "----------\n",
      "train Loss: 0.5208 Acc: 0.6844\n",
      "has spend time 74m 41s/n\n",
      "val Loss: 0.5519 Acc: 0.6993\n",
      "has spend time 74m 41s/n\n",
      "\n",
      "Epoch 2100/9999\n",
      "----------\n",
      "train Loss: 0.5178 Acc: 0.7336\n",
      "has spend time 74m 43s/n\n",
      "val Loss: 0.5509 Acc: 0.7124\n",
      "has spend time 74m 43s/n\n",
      "\n",
      "Epoch 2101/9999\n",
      "----------\n",
      "train Loss: 0.5256 Acc: 0.7254\n",
      "has spend time 74m 45s/n\n",
      "val Loss: 0.5524 Acc: 0.7190\n",
      "has spend time 74m 45s/n\n",
      "\n",
      "Epoch 2102/9999\n",
      "----------\n",
      "train Loss: 0.5216 Acc: 0.7459\n",
      "has spend time 74m 47s/n\n",
      "val Loss: 0.5539 Acc: 0.7190\n",
      "has spend time 74m 47s/n\n",
      "\n",
      "Epoch 2103/9999\n",
      "----------\n",
      "train Loss: 0.5129 Acc: 0.6926\n",
      "has spend time 74m 49s/n\n",
      "val Loss: 0.5706 Acc: 0.6928\n",
      "has spend time 74m 49s/n\n",
      "\n",
      "Epoch 2104/9999\n",
      "----------\n",
      "train Loss: 0.5204 Acc: 0.7295\n",
      "has spend time 74m 51s/n\n",
      "val Loss: 0.5470 Acc: 0.7255\n",
      "has spend time 74m 52s/n\n",
      "\n",
      "Epoch 2105/9999\n",
      "----------\n",
      "train Loss: 0.5351 Acc: 0.7090\n",
      "has spend time 74m 53s/n\n",
      "val Loss: 0.5470 Acc: 0.7059\n",
      "has spend time 74m 54s/n\n",
      "\n",
      "Epoch 2106/9999\n",
      "----------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train Loss: 0.5225 Acc: 0.7459\n",
      "has spend time 74m 56s/n\n",
      "val Loss: 0.5676 Acc: 0.6928\n",
      "has spend time 74m 56s/n\n",
      "\n",
      "Epoch 2107/9999\n",
      "----------\n",
      "train Loss: 0.4718 Acc: 0.7746\n",
      "has spend time 74m 58s/n\n",
      "val Loss: 0.5505 Acc: 0.7124\n",
      "has spend time 74m 58s/n\n",
      "\n",
      "Epoch 2108/9999\n",
      "----------\n",
      "train Loss: 0.4840 Acc: 0.7213\n",
      "has spend time 74m 60s/n\n",
      "val Loss: 0.5641 Acc: 0.6928\n",
      "has spend time 75m 0s/n\n",
      "\n",
      "Epoch 2109/9999\n",
      "----------\n",
      "train Loss: 0.4805 Acc: 0.7459\n",
      "has spend time 75m 2s/n\n",
      "val Loss: 0.5516 Acc: 0.6993\n",
      "has spend time 75m 2s/n\n",
      "\n",
      "Epoch 2110/9999\n",
      "----------\n",
      "train Loss: 0.4942 Acc: 0.7623\n",
      "has spend time 75m 4s/n\n",
      "val Loss: 0.5590 Acc: 0.6863\n",
      "has spend time 75m 4s/n\n",
      "\n",
      "Epoch 2111/9999\n",
      "----------\n",
      "train Loss: 0.4937 Acc: 0.7623\n",
      "has spend time 75m 6s/n\n",
      "val Loss: 0.5581 Acc: 0.7124\n",
      "has spend time 75m 6s/n\n",
      "\n",
      "Epoch 2112/9999\n",
      "----------\n",
      "train Loss: 0.5075 Acc: 0.7213\n",
      "has spend time 75m 8s/n\n",
      "val Loss: 0.5560 Acc: 0.6928\n",
      "has spend time 75m 8s/n\n",
      "\n",
      "Epoch 2113/9999\n",
      "----------\n",
      "train Loss: 0.4606 Acc: 0.7664\n",
      "has spend time 75m 10s/n\n",
      "val Loss: 0.5576 Acc: 0.6993\n",
      "has spend time 75m 10s/n\n",
      "\n",
      "Epoch 2114/9999\n",
      "----------\n",
      "train Loss: 0.5016 Acc: 0.7459\n",
      "has spend time 75m 12s/n\n",
      "val Loss: 0.5512 Acc: 0.7124\n",
      "has spend time 75m 12s/n\n",
      "\n",
      "Epoch 2115/9999\n",
      "----------\n",
      "train Loss: 0.4997 Acc: 0.7377\n",
      "has spend time 75m 14s/n\n",
      "val Loss: 0.5574 Acc: 0.6993\n",
      "has spend time 75m 15s/n\n",
      "\n",
      "Epoch 2116/9999\n",
      "----------\n",
      "train Loss: 0.4969 Acc: 0.7582\n",
      "has spend time 75m 16s/n\n",
      "val Loss: 0.5665 Acc: 0.6993\n",
      "has spend time 75m 17s/n\n",
      "\n",
      "Epoch 2117/9999\n",
      "----------\n",
      "train Loss: 0.5344 Acc: 0.7172\n",
      "has spend time 75m 19s/n\n",
      "val Loss: 0.5627 Acc: 0.6993\n",
      "has spend time 75m 19s/n\n",
      "\n",
      "Epoch 2118/9999\n",
      "----------\n",
      "train Loss: 0.5088 Acc: 0.7295\n",
      "has spend time 75m 21s/n\n",
      "val Loss: 0.5591 Acc: 0.6928\n",
      "has spend time 75m 21s/n\n",
      "\n",
      "Epoch 2119/9999\n",
      "----------\n",
      "train Loss: 0.5200 Acc: 0.7336\n",
      "has spend time 75m 23s/n\n",
      "val Loss: 0.5590 Acc: 0.7059\n",
      "has spend time 75m 23s/n\n",
      "\n",
      "Epoch 2120/9999\n",
      "----------\n",
      "train Loss: 0.5275 Acc: 0.7336\n",
      "has spend time 75m 25s/n\n",
      "val Loss: 0.5624 Acc: 0.6928\n",
      "has spend time 75m 25s/n\n",
      "\n",
      "Epoch 2121/9999\n",
      "----------\n",
      "train Loss: 0.5126 Acc: 0.7254\n",
      "has spend time 75m 27s/n\n",
      "val Loss: 0.5490 Acc: 0.7059\n",
      "has spend time 75m 28s/n\n",
      "\n",
      "Epoch 2122/9999\n",
      "----------\n",
      "train Loss: 0.5436 Acc: 0.6885\n",
      "has spend time 75m 29s/n\n",
      "val Loss: 0.5534 Acc: 0.7124\n",
      "has spend time 75m 30s/n\n",
      "\n",
      "Epoch 2123/9999\n",
      "----------\n",
      "train Loss: 0.4883 Acc: 0.7664\n",
      "has spend time 75m 31s/n\n",
      "val Loss: 0.5551 Acc: 0.6928\n",
      "has spend time 75m 32s/n\n",
      "\n",
      "Epoch 2124/9999\n",
      "----------\n",
      "train Loss: 0.5331 Acc: 0.6967\n",
      "has spend time 75m 34s/n\n",
      "val Loss: 0.5477 Acc: 0.6993\n",
      "has spend time 75m 34s/n\n",
      "\n",
      "Epoch 2125/9999\n",
      "----------\n",
      "train Loss: 0.5006 Acc: 0.7254\n",
      "has spend time 75m 36s/n\n",
      "val Loss: 0.5523 Acc: 0.7124\n",
      "has spend time 75m 36s/n\n",
      "\n",
      "Epoch 2126/9999\n",
      "----------\n",
      "train Loss: 0.5007 Acc: 0.7213\n",
      "has spend time 75m 38s/n\n",
      "val Loss: 0.5686 Acc: 0.6928\n",
      "has spend time 75m 38s/n\n",
      "\n",
      "Epoch 2127/9999\n",
      "----------\n",
      "train Loss: 0.5332 Acc: 0.7008\n",
      "has spend time 75m 40s/n\n",
      "val Loss: 0.5550 Acc: 0.6928\n",
      "has spend time 75m 41s/n\n",
      "\n",
      "Epoch 2128/9999\n",
      "----------\n",
      "train Loss: 0.5105 Acc: 0.7254\n",
      "has spend time 75m 42s/n\n",
      "val Loss: 0.5518 Acc: 0.6993\n",
      "has spend time 75m 43s/n\n",
      "\n",
      "Epoch 2129/9999\n",
      "----------\n",
      "train Loss: 0.5139 Acc: 0.7541\n",
      "has spend time 75m 44s/n\n",
      "val Loss: 0.5478 Acc: 0.7190\n",
      "has spend time 75m 45s/n\n",
      "\n",
      "Epoch 2130/9999\n",
      "----------\n",
      "train Loss: 0.5368 Acc: 0.7172\n",
      "has spend time 75m 46s/n\n",
      "val Loss: 0.5550 Acc: 0.7124\n",
      "has spend time 75m 47s/n\n",
      "\n",
      "Epoch 2131/9999\n",
      "----------\n",
      "train Loss: 0.5253 Acc: 0.7213\n",
      "has spend time 75m 48s/n\n",
      "val Loss: 0.5484 Acc: 0.7190\n",
      "has spend time 75m 49s/n\n",
      "\n",
      "Epoch 2132/9999\n",
      "----------\n",
      "train Loss: 0.4973 Acc: 0.7418\n",
      "has spend time 75m 50s/n\n",
      "val Loss: 0.5564 Acc: 0.6993\n",
      "has spend time 75m 51s/n\n",
      "\n",
      "Epoch 2133/9999\n",
      "----------\n",
      "train Loss: 0.5033 Acc: 0.7664\n",
      "has spend time 75m 52s/n\n",
      "val Loss: 0.5515 Acc: 0.7255\n",
      "has spend time 75m 53s/n\n",
      "\n",
      "Epoch 2134/9999\n",
      "----------\n",
      "train Loss: 0.5052 Acc: 0.7377\n",
      "has spend time 75m 55s/n\n",
      "val Loss: 0.5559 Acc: 0.7124\n",
      "has spend time 75m 55s/n\n",
      "\n",
      "Epoch 2135/9999\n",
      "----------\n",
      "train Loss: 0.4902 Acc: 0.7746\n",
      "has spend time 75m 57s/n\n",
      "val Loss: 0.5556 Acc: 0.6928\n",
      "has spend time 75m 57s/n\n",
      "\n",
      "Epoch 2136/9999\n",
      "----------\n",
      "train Loss: 0.5069 Acc: 0.7336\n",
      "has spend time 75m 59s/n\n",
      "val Loss: 0.5472 Acc: 0.7059\n",
      "has spend time 75m 59s/n\n",
      "\n",
      "Epoch 2137/9999\n",
      "----------\n",
      "train Loss: 0.5062 Acc: 0.7254\n",
      "has spend time 76m 1s/n\n",
      "val Loss: 0.5511 Acc: 0.7059\n",
      "has spend time 76m 1s/n\n",
      "\n",
      "Epoch 2138/9999\n",
      "----------\n",
      "train Loss: 0.5025 Acc: 0.7500\n",
      "has spend time 76m 3s/n\n",
      "val Loss: 0.5564 Acc: 0.7059\n",
      "has spend time 76m 4s/n\n",
      "\n",
      "Epoch 2139/9999\n",
      "----------\n",
      "train Loss: 0.5206 Acc: 0.6926\n",
      "has spend time 76m 5s/n\n",
      "val Loss: 0.5666 Acc: 0.6928\n",
      "has spend time 76m 6s/n\n",
      "\n",
      "Epoch 2140/9999\n",
      "----------\n",
      "train Loss: 0.5304 Acc: 0.7172\n",
      "has spend time 76m 7s/n\n",
      "val Loss: 0.5575 Acc: 0.6993\n",
      "has spend time 76m 8s/n\n",
      "\n",
      "Epoch 2141/9999\n",
      "----------\n",
      "train Loss: 0.5184 Acc: 0.7172\n",
      "has spend time 76m 9s/n\n",
      "val Loss: 0.5553 Acc: 0.7059\n",
      "has spend time 76m 10s/n\n",
      "\n",
      "Epoch 2142/9999\n",
      "----------\n",
      "train Loss: 0.4758 Acc: 0.7787\n",
      "has spend time 76m 12s/n\n",
      "val Loss: 0.5501 Acc: 0.7059\n",
      "has spend time 76m 13s/n\n",
      "\n",
      "Epoch 2143/9999\n",
      "----------\n",
      "train Loss: 0.5041 Acc: 0.7377\n",
      "has spend time 76m 14s/n\n",
      "val Loss: 0.5463 Acc: 0.7124\n",
      "has spend time 76m 15s/n\n",
      "\n",
      "Epoch 2144/9999\n",
      "----------\n",
      "train Loss: 0.5232 Acc: 0.7500\n",
      "has spend time 76m 16s/n\n",
      "val Loss: 0.5482 Acc: 0.7124\n",
      "has spend time 76m 17s/n\n",
      "\n",
      "Epoch 2145/9999\n",
      "----------\n",
      "train Loss: 0.5126 Acc: 0.7295\n",
      "has spend time 76m 18s/n\n",
      "val Loss: 0.5566 Acc: 0.6993\n",
      "has spend time 76m 19s/n\n",
      "\n",
      "Epoch 2146/9999\n",
      "----------\n",
      "train Loss: 0.4805 Acc: 0.7787\n",
      "has spend time 76m 20s/n\n",
      "val Loss: 0.5555 Acc: 0.7124\n",
      "has spend time 76m 21s/n\n",
      "\n",
      "Epoch 2147/9999\n",
      "----------\n",
      "train Loss: 0.5075 Acc: 0.7623\n",
      "has spend time 76m 22s/n\n",
      "val Loss: 0.5538 Acc: 0.7059\n",
      "has spend time 76m 23s/n\n",
      "\n",
      "Epoch 2148/9999\n",
      "----------\n",
      "train Loss: 0.4844 Acc: 0.7828\n",
      "has spend time 76m 25s/n\n",
      "val Loss: 0.5464 Acc: 0.7059\n",
      "has spend time 76m 26s/n\n",
      "\n",
      "Epoch 2149/9999\n",
      "----------\n",
      "train Loss: 0.4933 Acc: 0.7623\n",
      "has spend time 76m 27s/n\n",
      "val Loss: 0.5454 Acc: 0.7059\n",
      "has spend time 76m 28s/n\n",
      "\n",
      "Epoch 2150/9999\n",
      "----------\n",
      "train Loss: 0.5022 Acc: 0.7787\n",
      "has spend time 76m 29s/n\n",
      "val Loss: 0.5494 Acc: 0.7124\n",
      "has spend time 76m 30s/n\n",
      "\n",
      "Epoch 2151/9999\n",
      "----------\n",
      "train Loss: 0.5321 Acc: 0.7336\n",
      "has spend time 76m 31s/n\n",
      "val Loss: 0.5419 Acc: 0.7124\n",
      "has spend time 76m 32s/n\n",
      "\n",
      "Epoch 2152/9999\n",
      "----------\n",
      "train Loss: 0.5147 Acc: 0.7541\n",
      "has spend time 76m 33s/n\n",
      "val Loss: 0.5522 Acc: 0.7124\n",
      "has spend time 76m 34s/n\n",
      "\n",
      "Epoch 2153/9999\n",
      "----------\n",
      "train Loss: 0.5333 Acc: 0.7213\n",
      "has spend time 76m 35s/n\n",
      "val Loss: 0.5491 Acc: 0.7124\n",
      "has spend time 76m 36s/n\n",
      "\n",
      "Epoch 2154/9999\n",
      "----------\n",
      "train Loss: 0.5125 Acc: 0.7418\n",
      "has spend time 76m 37s/n\n",
      "val Loss: 0.5590 Acc: 0.6993\n",
      "has spend time 76m 38s/n\n",
      "\n",
      "Epoch 2155/9999\n",
      "----------\n",
      "train Loss: 0.4800 Acc: 0.8033\n",
      "has spend time 76m 40s/n\n",
      "val Loss: 0.5653 Acc: 0.6928\n",
      "has spend time 76m 40s/n\n",
      "\n",
      "Epoch 2156/9999\n",
      "----------\n",
      "train Loss: 0.4934 Acc: 0.7705\n",
      "has spend time 76m 42s/n\n",
      "val Loss: 0.5514 Acc: 0.6993\n",
      "has spend time 76m 42s/n\n",
      "\n",
      "Epoch 2157/9999\n",
      "----------\n",
      "train Loss: 0.5241 Acc: 0.7418\n",
      "has spend time 76m 44s/n\n",
      "val Loss: 0.5491 Acc: 0.7124\n",
      "has spend time 76m 44s/n\n",
      "\n",
      "Epoch 2158/9999\n",
      "----------\n",
      "train Loss: 0.5338 Acc: 0.7049\n",
      "has spend time 76m 46s/n\n",
      "val Loss: 0.5777 Acc: 0.6928\n",
      "has spend time 76m 46s/n\n",
      "\n",
      "Epoch 2159/9999\n",
      "----------\n",
      "train Loss: 0.4946 Acc: 0.7459\n",
      "has spend time 76m 48s/n\n",
      "val Loss: 0.5471 Acc: 0.7124\n",
      "has spend time 76m 48s/n\n",
      "\n",
      "Epoch 2160/9999\n",
      "----------\n",
      "train Loss: 0.5466 Acc: 0.7172\n",
      "has spend time 76m 50s/n\n",
      "val Loss: 0.5495 Acc: 0.7255\n",
      "has spend time 76m 50s/n\n",
      "\n",
      "Epoch 2161/9999\n",
      "----------\n",
      "train Loss: 0.4996 Acc: 0.7377\n",
      "has spend time 76m 52s/n\n",
      "val Loss: 0.5495 Acc: 0.7190\n",
      "has spend time 76m 53s/n\n",
      "\n",
      "Epoch 2162/9999\n",
      "----------\n",
      "train Loss: 0.5389 Acc: 0.7336\n",
      "has spend time 76m 54s/n\n",
      "val Loss: 0.5529 Acc: 0.7059\n",
      "has spend time 76m 55s/n\n",
      "\n",
      "Epoch 2163/9999\n",
      "----------\n",
      "train Loss: 0.5057 Acc: 0.7459\n",
      "has spend time 76m 56s/n\n",
      "val Loss: 0.5508 Acc: 0.7059\n",
      "has spend time 76m 57s/n\n",
      "\n",
      "Epoch 2164/9999\n",
      "----------\n",
      "train Loss: 0.5239 Acc: 0.7541\n",
      "has spend time 76m 58s/n\n",
      "val Loss: 0.5617 Acc: 0.6928\n",
      "has spend time 76m 59s/n\n",
      "\n",
      "Epoch 2165/9999\n",
      "----------\n",
      "train Loss: 0.5070 Acc: 0.7172\n",
      "has spend time 77m 0s/n\n",
      "val Loss: 0.5592 Acc: 0.6993\n",
      "has spend time 77m 1s/n\n",
      "\n",
      "Epoch 2166/9999\n",
      "----------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train Loss: 0.5084 Acc: 0.7500\n",
      "has spend time 77m 2s/n\n",
      "val Loss: 0.5445 Acc: 0.7059\n",
      "has spend time 77m 3s/n\n",
      "\n",
      "Epoch 2167/9999\n",
      "----------\n",
      "train Loss: 0.4999 Acc: 0.7705\n",
      "has spend time 77m 5s/n\n",
      "val Loss: 0.5520 Acc: 0.7190\n",
      "has spend time 77m 5s/n\n",
      "\n",
      "Epoch 2168/9999\n",
      "----------\n",
      "train Loss: 0.5185 Acc: 0.7418\n",
      "has spend time 77m 7s/n\n",
      "val Loss: 0.5467 Acc: 0.6993\n",
      "has spend time 77m 8s/n\n",
      "\n",
      "Epoch 2169/9999\n",
      "----------\n",
      "train Loss: 0.5515 Acc: 0.6926\n",
      "has spend time 77m 9s/n\n",
      "val Loss: 0.5403 Acc: 0.7190\n",
      "has spend time 77m 10s/n\n",
      "\n",
      "Epoch 2170/9999\n",
      "----------\n",
      "train Loss: 0.5398 Acc: 0.7213\n",
      "has spend time 77m 11s/n\n",
      "val Loss: 0.5426 Acc: 0.7190\n",
      "has spend time 77m 12s/n\n",
      "\n",
      "Epoch 2171/9999\n",
      "----------\n",
      "train Loss: 0.4877 Acc: 0.7541\n",
      "has spend time 77m 13s/n\n",
      "val Loss: 0.5533 Acc: 0.7190\n",
      "has spend time 77m 14s/n\n",
      "\n",
      "Epoch 2172/9999\n",
      "----------\n",
      "train Loss: 0.5067 Acc: 0.7090\n",
      "has spend time 77m 15s/n\n",
      "val Loss: 0.5442 Acc: 0.6993\n",
      "has spend time 77m 16s/n\n",
      "\n",
      "Epoch 2173/9999\n",
      "----------\n",
      "train Loss: 0.4506 Acc: 0.7623\n",
      "has spend time 77m 18s/n\n",
      "val Loss: 0.5492 Acc: 0.7124\n",
      "has spend time 77m 18s/n\n",
      "\n",
      "Epoch 2174/9999\n",
      "----------\n",
      "train Loss: 0.4944 Acc: 0.7418\n",
      "has spend time 77m 20s/n\n",
      "val Loss: 0.5482 Acc: 0.7124\n",
      "has spend time 77m 20s/n\n",
      "\n",
      "Epoch 2175/9999\n",
      "----------\n",
      "train Loss: 0.5382 Acc: 0.7254\n",
      "has spend time 77m 22s/n\n",
      "val Loss: 0.5617 Acc: 0.6928\n",
      "has spend time 77m 22s/n\n",
      "\n",
      "Epoch 2176/9999\n",
      "----------\n",
      "train Loss: 0.5004 Acc: 0.7336\n",
      "has spend time 77m 24s/n\n",
      "val Loss: 0.5504 Acc: 0.7124\n",
      "has spend time 77m 24s/n\n",
      "\n",
      "Epoch 2177/9999\n",
      "----------\n",
      "train Loss: 0.4815 Acc: 0.7541\n",
      "has spend time 77m 26s/n\n",
      "val Loss: 0.5470 Acc: 0.7059\n",
      "has spend time 77m 26s/n\n",
      "\n",
      "Epoch 2178/9999\n",
      "----------\n",
      "train Loss: 0.5039 Acc: 0.7500\n",
      "has spend time 77m 28s/n\n",
      "val Loss: 0.5530 Acc: 0.6863\n",
      "has spend time 77m 28s/n\n",
      "\n",
      "Epoch 2179/9999\n",
      "----------\n",
      "train Loss: 0.5203 Acc: 0.7090\n",
      "has spend time 77m 30s/n\n",
      "val Loss: 0.5520 Acc: 0.7059\n",
      "has spend time 77m 30s/n\n",
      "\n",
      "Epoch 2180/9999\n",
      "----------\n",
      "train Loss: 0.5087 Acc: 0.7787\n",
      "has spend time 77m 32s/n\n",
      "val Loss: 0.5423 Acc: 0.7255\n",
      "has spend time 77m 33s/n\n",
      "\n",
      "Epoch 2181/9999\n",
      "----------\n",
      "train Loss: 0.5046 Acc: 0.7418\n",
      "has spend time 77m 34s/n\n",
      "val Loss: 0.5454 Acc: 0.7124\n",
      "has spend time 77m 35s/n\n",
      "\n",
      "Epoch 2182/9999\n",
      "----------\n",
      "train Loss: 0.4801 Acc: 0.7623\n",
      "has spend time 77m 36s/n\n",
      "val Loss: 0.5545 Acc: 0.7059\n",
      "has spend time 77m 37s/n\n",
      "\n",
      "Epoch 2183/9999\n",
      "----------\n",
      "train Loss: 0.4590 Acc: 0.8115\n",
      "has spend time 77m 38s/n\n",
      "val Loss: 0.5436 Acc: 0.6993\n",
      "has spend time 77m 39s/n\n",
      "\n",
      "Epoch 2184/9999\n",
      "----------\n",
      "train Loss: 0.5353 Acc: 0.7172\n",
      "has spend time 77m 40s/n\n",
      "val Loss: 0.5454 Acc: 0.7124\n",
      "has spend time 77m 41s/n\n",
      "\n",
      "Epoch 2185/9999\n",
      "----------\n",
      "train Loss: 0.5182 Acc: 0.7049\n",
      "has spend time 77m 43s/n\n",
      "val Loss: 0.5460 Acc: 0.7190\n",
      "has spend time 77m 43s/n\n",
      "\n",
      "Epoch 2186/9999\n",
      "----------\n",
      "train Loss: 0.4950 Acc: 0.7377\n",
      "has spend time 77m 45s/n\n",
      "val Loss: 0.5520 Acc: 0.7190\n",
      "has spend time 77m 45s/n\n",
      "\n",
      "Epoch 2187/9999\n",
      "----------\n",
      "train Loss: 0.4969 Acc: 0.7623\n",
      "has spend time 77m 47s/n\n",
      "val Loss: 0.5512 Acc: 0.7059\n",
      "has spend time 77m 47s/n\n",
      "\n",
      "Epoch 2188/9999\n",
      "----------\n",
      "train Loss: 0.5237 Acc: 0.7213\n",
      "has spend time 77m 49s/n\n",
      "val Loss: 0.5551 Acc: 0.6993\n",
      "has spend time 77m 49s/n\n",
      "\n",
      "Epoch 2189/9999\n",
      "----------\n",
      "train Loss: 0.4790 Acc: 0.7377\n",
      "has spend time 77m 51s/n\n",
      "val Loss: 0.5453 Acc: 0.7124\n",
      "has spend time 77m 52s/n\n",
      "\n",
      "Epoch 2190/9999\n",
      "----------\n",
      "train Loss: 0.4980 Acc: 0.7459\n",
      "has spend time 77m 53s/n\n",
      "val Loss: 0.5415 Acc: 0.7190\n",
      "has spend time 77m 54s/n\n",
      "\n",
      "Epoch 2191/9999\n",
      "----------\n",
      "train Loss: 0.4991 Acc: 0.7582\n",
      "has spend time 77m 56s/n\n",
      "val Loss: 0.5400 Acc: 0.7124\n",
      "has spend time 77m 56s/n\n",
      "\n",
      "Epoch 2192/9999\n",
      "----------\n",
      "train Loss: 0.5110 Acc: 0.7090\n",
      "has spend time 77m 58s/n\n",
      "val Loss: 0.5554 Acc: 0.7059\n",
      "has spend time 77m 58s/n\n",
      "\n",
      "Epoch 2193/9999\n",
      "----------\n",
      "train Loss: 0.5087 Acc: 0.7336\n",
      "has spend time 77m 60s/n\n",
      "val Loss: 0.5449 Acc: 0.7190\n",
      "has spend time 78m 1s/n\n",
      "\n",
      "Epoch 2194/9999\n",
      "----------\n",
      "train Loss: 0.5157 Acc: 0.7090\n",
      "has spend time 78m 2s/n\n",
      "val Loss: 0.5489 Acc: 0.7190\n",
      "has spend time 78m 3s/n\n",
      "\n",
      "Epoch 2195/9999\n",
      "----------\n",
      "train Loss: 0.4817 Acc: 0.7459\n",
      "has spend time 78m 4s/n\n",
      "val Loss: 0.5439 Acc: 0.7124\n",
      "has spend time 78m 5s/n\n",
      "\n",
      "Epoch 2196/9999\n",
      "----------\n",
      "train Loss: 0.5318 Acc: 0.7008\n",
      "has spend time 78m 6s/n\n",
      "val Loss: 0.5468 Acc: 0.7059\n",
      "has spend time 78m 7s/n\n",
      "\n",
      "Epoch 2197/9999\n",
      "----------\n",
      "train Loss: 0.4913 Acc: 0.7459\n",
      "has spend time 78m 8s/n\n",
      "val Loss: 0.5500 Acc: 0.7124\n",
      "has spend time 78m 9s/n\n",
      "\n",
      "Epoch 2198/9999\n",
      "----------\n",
      "train Loss: 0.5295 Acc: 0.7049\n",
      "has spend time 78m 10s/n\n",
      "val Loss: 0.5454 Acc: 0.7124\n",
      "has spend time 78m 11s/n\n",
      "\n",
      "Epoch 2199/9999\n",
      "----------\n",
      "train Loss: 0.5093 Acc: 0.7336\n",
      "has spend time 78m 12s/n\n",
      "val Loss: 0.5445 Acc: 0.7190\n",
      "has spend time 78m 13s/n\n",
      "\n",
      "Epoch 2200/9999\n",
      "----------\n",
      "train Loss: 0.4771 Acc: 0.7541\n",
      "has spend time 78m 14s/n\n",
      "val Loss: 0.5525 Acc: 0.7124\n",
      "has spend time 78m 15s/n\n",
      "\n",
      "Epoch 2201/9999\n",
      "----------\n",
      "train Loss: 0.5229 Acc: 0.7500\n",
      "has spend time 78m 17s/n\n",
      "val Loss: 0.5574 Acc: 0.7190\n",
      "has spend time 78m 17s/n\n",
      "\n",
      "Epoch 2202/9999\n",
      "----------\n",
      "train Loss: 0.5001 Acc: 0.7336\n",
      "has spend time 78m 19s/n\n",
      "val Loss: 0.5560 Acc: 0.6993\n",
      "has spend time 78m 19s/n\n",
      "\n",
      "Epoch 2203/9999\n",
      "----------\n",
      "train Loss: 0.5192 Acc: 0.7213\n",
      "has spend time 78m 21s/n\n",
      "val Loss: 0.5525 Acc: 0.7124\n",
      "has spend time 78m 21s/n\n",
      "\n",
      "Epoch 2204/9999\n",
      "----------\n",
      "train Loss: 0.5350 Acc: 0.7459\n",
      "has spend time 78m 23s/n\n",
      "val Loss: 0.5562 Acc: 0.7059\n",
      "has spend time 78m 23s/n\n",
      "\n",
      "Epoch 2205/9999\n",
      "----------\n",
      "train Loss: 0.5056 Acc: 0.7541\n",
      "has spend time 78m 25s/n\n",
      "val Loss: 0.5423 Acc: 0.7255\n",
      "has spend time 78m 25s/n\n",
      "\n",
      "Epoch 2206/9999\n",
      "----------\n",
      "train Loss: 0.4965 Acc: 0.7705\n",
      "has spend time 78m 27s/n\n",
      "val Loss: 0.5588 Acc: 0.6993\n",
      "has spend time 78m 27s/n\n",
      "\n",
      "Epoch 2207/9999\n",
      "----------\n",
      "train Loss: 0.5268 Acc: 0.6967\n",
      "has spend time 78m 29s/n\n",
      "val Loss: 0.5509 Acc: 0.6993\n",
      "has spend time 78m 30s/n\n",
      "\n",
      "Epoch 2208/9999\n",
      "----------\n",
      "train Loss: 0.5244 Acc: 0.7131\n",
      "has spend time 78m 31s/n\n",
      "val Loss: 0.5563 Acc: 0.6928\n",
      "has spend time 78m 32s/n\n",
      "\n",
      "Epoch 2209/9999\n",
      "----------\n",
      "train Loss: 0.5026 Acc: 0.7213\n",
      "has spend time 78m 33s/n\n",
      "val Loss: 0.5560 Acc: 0.6993\n",
      "has spend time 78m 34s/n\n",
      "\n",
      "Epoch 2210/9999\n",
      "----------\n",
      "train Loss: 0.5088 Acc: 0.7541\n",
      "has spend time 78m 36s/n\n",
      "val Loss: 0.5553 Acc: 0.6993\n",
      "has spend time 78m 37s/n\n",
      "\n",
      "Epoch 2211/9999\n",
      "----------\n",
      "train Loss: 0.5057 Acc: 0.7377\n",
      "has spend time 78m 38s/n\n",
      "val Loss: 0.5544 Acc: 0.6993\n",
      "has spend time 78m 39s/n\n",
      "\n",
      "Epoch 2212/9999\n",
      "----------\n",
      "train Loss: 0.4899 Acc: 0.7582\n",
      "has spend time 78m 40s/n\n",
      "val Loss: 0.5533 Acc: 0.6993\n",
      "has spend time 78m 41s/n\n",
      "\n",
      "Epoch 2213/9999\n",
      "----------\n",
      "train Loss: 0.5123 Acc: 0.6967\n",
      "has spend time 78m 43s/n\n",
      "val Loss: 0.5656 Acc: 0.7059\n",
      "has spend time 78m 43s/n\n",
      "\n",
      "Epoch 2214/9999\n",
      "----------\n",
      "train Loss: 0.4890 Acc: 0.7705\n",
      "has spend time 78m 45s/n\n",
      "val Loss: 0.5529 Acc: 0.7059\n",
      "has spend time 78m 45s/n\n",
      "\n",
      "Epoch 2215/9999\n",
      "----------\n",
      "train Loss: 0.4887 Acc: 0.7582\n",
      "has spend time 78m 47s/n\n",
      "val Loss: 0.5563 Acc: 0.7059\n",
      "has spend time 78m 47s/n\n",
      "\n",
      "Epoch 2216/9999\n",
      "----------\n",
      "train Loss: 0.5180 Acc: 0.7459\n",
      "has spend time 78m 49s/n\n",
      "val Loss: 0.5534 Acc: 0.7255\n",
      "has spend time 78m 49s/n\n",
      "\n",
      "Epoch 2217/9999\n",
      "----------\n",
      "train Loss: 0.4900 Acc: 0.7500\n",
      "has spend time 78m 51s/n\n",
      "val Loss: 0.5483 Acc: 0.7059\n",
      "has spend time 78m 51s/n\n",
      "\n",
      "Epoch 2218/9999\n",
      "----------\n",
      "train Loss: 0.4921 Acc: 0.7705\n",
      "has spend time 78m 53s/n\n",
      "val Loss: 0.5534 Acc: 0.7190\n",
      "has spend time 78m 53s/n\n",
      "\n",
      "Epoch 2219/9999\n",
      "----------\n",
      "train Loss: 0.4994 Acc: 0.7336\n",
      "has spend time 78m 55s/n\n",
      "val Loss: 0.5498 Acc: 0.7059\n",
      "has spend time 78m 55s/n\n",
      "\n",
      "Epoch 2220/9999\n",
      "----------\n",
      "train Loss: 0.5115 Acc: 0.7172\n",
      "has spend time 78m 57s/n\n",
      "val Loss: 0.5383 Acc: 0.7255\n",
      "has spend time 78m 57s/n\n",
      "\n",
      "Epoch 2221/9999\n",
      "----------\n",
      "train Loss: 0.5170 Acc: 0.7500\n",
      "has spend time 78m 59s/n\n",
      "val Loss: 0.5480 Acc: 0.7124\n",
      "has spend time 78m 60s/n\n",
      "\n",
      "Epoch 2222/9999\n",
      "----------\n",
      "train Loss: 0.5076 Acc: 0.7500\n",
      "has spend time 79m 1s/n\n",
      "val Loss: 0.5447 Acc: 0.7190\n",
      "has spend time 79m 2s/n\n",
      "\n",
      "Epoch 2223/9999\n",
      "----------\n",
      "train Loss: 0.5103 Acc: 0.7377\n",
      "has spend time 79m 3s/n\n",
      "val Loss: 0.5483 Acc: 0.7190\n",
      "has spend time 79m 4s/n\n",
      "\n",
      "Epoch 2224/9999\n",
      "----------\n",
      "train Loss: 0.4913 Acc: 0.7541\n",
      "has spend time 79m 5s/n\n",
      "val Loss: 0.5681 Acc: 0.6928\n",
      "has spend time 79m 6s/n\n",
      "\n",
      "Epoch 2225/9999\n",
      "----------\n",
      "train Loss: 0.5018 Acc: 0.7254\n",
      "has spend time 79m 7s/n\n",
      "val Loss: 0.5446 Acc: 0.7124\n",
      "has spend time 79m 8s/n\n",
      "\n",
      "Epoch 2226/9999\n",
      "----------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train Loss: 0.5353 Acc: 0.7049\n",
      "has spend time 79m 10s/n\n",
      "val Loss: 0.5669 Acc: 0.6993\n",
      "has spend time 79m 10s/n\n",
      "\n",
      "Epoch 2227/9999\n",
      "----------\n",
      "train Loss: 0.4994 Acc: 0.7500\n",
      "has spend time 79m 12s/n\n",
      "val Loss: 0.5529 Acc: 0.6928\n",
      "has spend time 79m 13s/n\n",
      "\n",
      "Epoch 2228/9999\n",
      "----------\n",
      "train Loss: 0.4964 Acc: 0.7377\n",
      "has spend time 79m 14s/n\n",
      "val Loss: 0.5519 Acc: 0.7124\n",
      "has spend time 79m 15s/n\n",
      "\n",
      "Epoch 2229/9999\n",
      "----------\n",
      "train Loss: 0.5185 Acc: 0.7254\n",
      "has spend time 79m 16s/n\n",
      "val Loss: 0.5536 Acc: 0.7059\n",
      "has spend time 79m 17s/n\n",
      "\n",
      "Epoch 2230/9999\n",
      "----------\n",
      "train Loss: 0.5049 Acc: 0.7623\n",
      "has spend time 79m 18s/n\n",
      "val Loss: 0.5495 Acc: 0.7190\n",
      "has spend time 79m 19s/n\n",
      "\n",
      "Epoch 2231/9999\n",
      "----------\n",
      "train Loss: 0.5310 Acc: 0.6967\n",
      "has spend time 79m 20s/n\n",
      "val Loss: 0.5512 Acc: 0.7059\n",
      "has spend time 79m 21s/n\n",
      "\n",
      "Epoch 2232/9999\n",
      "----------\n",
      "train Loss: 0.5223 Acc: 0.7172\n",
      "has spend time 79m 23s/n\n",
      "val Loss: 0.5519 Acc: 0.6928\n",
      "has spend time 79m 23s/n\n",
      "\n",
      "Epoch 2233/9999\n",
      "----------\n",
      "train Loss: 0.4960 Acc: 0.7459\n",
      "has spend time 79m 25s/n\n",
      "val Loss: 0.5561 Acc: 0.7059\n",
      "has spend time 79m 26s/n\n",
      "\n",
      "Epoch 2234/9999\n",
      "----------\n",
      "train Loss: 0.5044 Acc: 0.7500\n",
      "has spend time 79m 27s/n\n",
      "val Loss: 0.5497 Acc: 0.6993\n",
      "has spend time 79m 28s/n\n",
      "\n",
      "Epoch 2235/9999\n",
      "----------\n",
      "train Loss: 0.5087 Acc: 0.7582\n",
      "has spend time 79m 29s/n\n",
      "val Loss: 0.5634 Acc: 0.6928\n",
      "has spend time 79m 30s/n\n",
      "\n",
      "Epoch 2236/9999\n",
      "----------\n",
      "train Loss: 0.5038 Acc: 0.7500\n",
      "has spend time 79m 31s/n\n",
      "val Loss: 0.5715 Acc: 0.6993\n",
      "has spend time 79m 32s/n\n",
      "\n",
      "Epoch 2237/9999\n",
      "----------\n",
      "train Loss: 0.4663 Acc: 0.7869\n",
      "has spend time 79m 33s/n\n",
      "val Loss: 0.5586 Acc: 0.7059\n",
      "has spend time 79m 34s/n\n",
      "\n",
      "Epoch 2238/9999\n",
      "----------\n",
      "train Loss: 0.5151 Acc: 0.7295\n",
      "has spend time 79m 35s/n\n",
      "val Loss: 0.5540 Acc: 0.7124\n",
      "has spend time 79m 36s/n\n",
      "\n",
      "Epoch 2239/9999\n",
      "----------\n",
      "train Loss: 0.5167 Acc: 0.7664\n",
      "has spend time 79m 37s/n\n",
      "val Loss: 0.5496 Acc: 0.7059\n",
      "has spend time 79m 38s/n\n",
      "\n",
      "Epoch 2240/9999\n",
      "----------\n",
      "train Loss: 0.5083 Acc: 0.7459\n",
      "has spend time 79m 39s/n\n",
      "val Loss: 0.5526 Acc: 0.7190\n",
      "has spend time 79m 40s/n\n",
      "\n",
      "Epoch 2241/9999\n",
      "----------\n",
      "train Loss: 0.5023 Acc: 0.7254\n",
      "has spend time 79m 42s/n\n",
      "val Loss: 0.5437 Acc: 0.7190\n",
      "has spend time 79m 42s/n\n",
      "\n",
      "Epoch 2242/9999\n",
      "----------\n",
      "train Loss: 0.5014 Acc: 0.7623\n",
      "has spend time 79m 44s/n\n",
      "val Loss: 0.5472 Acc: 0.7059\n",
      "has spend time 79m 44s/n\n",
      "\n",
      "Epoch 2243/9999\n",
      "----------\n",
      "train Loss: 0.5110 Acc: 0.7131\n",
      "has spend time 79m 46s/n\n",
      "val Loss: 0.5459 Acc: 0.7190\n",
      "has spend time 79m 46s/n\n",
      "\n",
      "Epoch 2244/9999\n",
      "----------\n",
      "train Loss: 0.5091 Acc: 0.7500\n",
      "has spend time 79m 48s/n\n",
      "val Loss: 0.5487 Acc: 0.7190\n",
      "has spend time 79m 49s/n\n",
      "\n",
      "Epoch 2245/9999\n",
      "----------\n",
      "train Loss: 0.5423 Acc: 0.7131\n",
      "has spend time 79m 50s/n\n",
      "val Loss: 0.5704 Acc: 0.6993\n",
      "has spend time 79m 51s/n\n",
      "\n",
      "Epoch 2246/9999\n",
      "----------\n",
      "train Loss: 0.5295 Acc: 0.7336\n",
      "has spend time 79m 53s/n\n",
      "val Loss: 0.5496 Acc: 0.6928\n",
      "has spend time 79m 53s/n\n",
      "\n",
      "Epoch 2247/9999\n",
      "----------\n",
      "train Loss: 0.5233 Acc: 0.7090\n",
      "has spend time 79m 55s/n\n",
      "val Loss: 0.5439 Acc: 0.7255\n",
      "has spend time 79m 55s/n\n",
      "\n",
      "Epoch 2248/9999\n",
      "----------\n",
      "train Loss: 0.5184 Acc: 0.7295\n",
      "has spend time 79m 57s/n\n",
      "val Loss: 0.5707 Acc: 0.6928\n",
      "has spend time 79m 58s/n\n",
      "\n",
      "Epoch 2249/9999\n",
      "----------\n",
      "train Loss: 0.5018 Acc: 0.7213\n",
      "has spend time 79m 59s/n\n",
      "val Loss: 0.5757 Acc: 0.6863\n",
      "has spend time 79m 60s/n\n",
      "\n",
      "Epoch 2250/9999\n",
      "----------\n",
      "train Loss: 0.4892 Acc: 0.7500\n",
      "has spend time 80m 1s/n\n",
      "val Loss: 0.5426 Acc: 0.7124\n",
      "has spend time 80m 2s/n\n",
      "\n",
      "Epoch 2251/9999\n",
      "----------\n",
      "train Loss: 0.5043 Acc: 0.7295\n",
      "has spend time 80m 3s/n\n",
      "val Loss: 0.5496 Acc: 0.7124\n",
      "has spend time 80m 4s/n\n",
      "\n",
      "Epoch 2252/9999\n",
      "----------\n",
      "train Loss: 0.5008 Acc: 0.7541\n",
      "has spend time 80m 5s/n\n",
      "val Loss: 0.5600 Acc: 0.6993\n",
      "has spend time 80m 6s/n\n",
      "\n",
      "Epoch 2253/9999\n",
      "----------\n",
      "train Loss: 0.5271 Acc: 0.7254\n",
      "has spend time 80m 7s/n\n",
      "val Loss: 0.5541 Acc: 0.6928\n",
      "has spend time 80m 8s/n\n",
      "\n",
      "Epoch 2254/9999\n",
      "----------\n",
      "train Loss: 0.5121 Acc: 0.7254\n",
      "has spend time 80m 10s/n\n",
      "val Loss: 0.5510 Acc: 0.7255\n",
      "has spend time 80m 10s/n\n",
      "\n",
      "Epoch 2255/9999\n",
      "----------\n",
      "train Loss: 0.5094 Acc: 0.7541\n",
      "has spend time 80m 12s/n\n",
      "val Loss: 0.5840 Acc: 0.6928\n",
      "has spend time 80m 12s/n\n",
      "\n",
      "Epoch 2256/9999\n",
      "----------\n",
      "train Loss: 0.5149 Acc: 0.7090\n",
      "has spend time 80m 14s/n\n",
      "val Loss: 0.5514 Acc: 0.7059\n",
      "has spend time 80m 14s/n\n",
      "\n",
      "Epoch 2257/9999\n",
      "----------\n",
      "train Loss: 0.5046 Acc: 0.7295\n",
      "has spend time 80m 16s/n\n",
      "val Loss: 0.5420 Acc: 0.7124\n",
      "has spend time 80m 17s/n\n",
      "\n",
      "Epoch 2258/9999\n",
      "----------\n",
      "train Loss: 0.5099 Acc: 0.7254\n",
      "has spend time 80m 18s/n\n",
      "val Loss: 0.5455 Acc: 0.7190\n",
      "has spend time 80m 19s/n\n",
      "\n",
      "Epoch 2259/9999\n",
      "----------\n",
      "train Loss: 0.5078 Acc: 0.7500\n",
      "has spend time 80m 21s/n\n",
      "val Loss: 0.5494 Acc: 0.7190\n",
      "has spend time 80m 21s/n\n",
      "\n",
      "Epoch 2260/9999\n",
      "----------\n",
      "train Loss: 0.5173 Acc: 0.7418\n",
      "has spend time 80m 23s/n\n",
      "val Loss: 0.5375 Acc: 0.7124\n",
      "has spend time 80m 23s/n\n",
      "\n",
      "Epoch 2261/9999\n",
      "----------\n",
      "train Loss: 0.5238 Acc: 0.7336\n",
      "has spend time 80m 25s/n\n",
      "val Loss: 0.5567 Acc: 0.6928\n",
      "has spend time 80m 25s/n\n",
      "\n",
      "Epoch 2262/9999\n",
      "----------\n",
      "train Loss: 0.4916 Acc: 0.7500\n",
      "has spend time 80m 27s/n\n",
      "val Loss: 0.5532 Acc: 0.7124\n",
      "has spend time 80m 27s/n\n",
      "\n",
      "Epoch 2263/9999\n",
      "----------\n",
      "train Loss: 0.5148 Acc: 0.7418\n",
      "has spend time 80m 29s/n\n",
      "val Loss: 0.5483 Acc: 0.7124\n",
      "has spend time 80m 30s/n\n",
      "\n",
      "Epoch 2264/9999\n",
      "----------\n",
      "train Loss: 0.4943 Acc: 0.7459\n",
      "has spend time 80m 31s/n\n",
      "val Loss: 0.5451 Acc: 0.7255\n",
      "has spend time 80m 32s/n\n",
      "\n",
      "Epoch 2265/9999\n",
      "----------\n",
      "train Loss: 0.4969 Acc: 0.7664\n",
      "has spend time 80m 33s/n\n",
      "val Loss: 0.5532 Acc: 0.7059\n",
      "has spend time 80m 34s/n\n",
      "\n",
      "Epoch 2266/9999\n",
      "----------\n",
      "train Loss: 0.5004 Acc: 0.7295\n",
      "has spend time 80m 36s/n\n",
      "val Loss: 0.5443 Acc: 0.7255\n",
      "has spend time 80m 36s/n\n",
      "\n",
      "Epoch 2267/9999\n",
      "----------\n",
      "train Loss: 0.5134 Acc: 0.7172\n",
      "has spend time 80m 38s/n\n",
      "val Loss: 0.5661 Acc: 0.6928\n",
      "has spend time 80m 38s/n\n",
      "\n",
      "Epoch 2268/9999\n",
      "----------\n",
      "train Loss: 0.4887 Acc: 0.7418\n",
      "has spend time 80m 40s/n\n",
      "val Loss: 0.5558 Acc: 0.7124\n",
      "has spend time 80m 40s/n\n",
      "\n",
      "Epoch 2269/9999\n",
      "----------\n",
      "train Loss: 0.5153 Acc: 0.7295\n",
      "has spend time 80m 42s/n\n",
      "val Loss: 0.5448 Acc: 0.7124\n",
      "has spend time 80m 42s/n\n",
      "\n",
      "Epoch 2270/9999\n",
      "----------\n",
      "train Loss: 0.4644 Acc: 0.7951\n",
      "has spend time 80m 44s/n\n",
      "val Loss: 0.5508 Acc: 0.6993\n",
      "has spend time 80m 45s/n\n",
      "\n",
      "Epoch 2271/9999\n",
      "----------\n",
      "train Loss: 0.4993 Acc: 0.7213\n",
      "has spend time 80m 46s/n\n",
      "val Loss: 0.5480 Acc: 0.7124\n",
      "has spend time 80m 47s/n\n",
      "\n",
      "Epoch 2272/9999\n",
      "----------\n",
      "train Loss: 0.5094 Acc: 0.7500\n",
      "has spend time 80m 48s/n\n",
      "val Loss: 0.5692 Acc: 0.6863\n",
      "has spend time 80m 49s/n\n",
      "\n",
      "Epoch 2273/9999\n",
      "----------\n",
      "train Loss: 0.4799 Acc: 0.7582\n",
      "has spend time 80m 50s/n\n",
      "val Loss: 0.5646 Acc: 0.7059\n",
      "has spend time 80m 51s/n\n",
      "\n",
      "Epoch 2274/9999\n",
      "----------\n",
      "train Loss: 0.5511 Acc: 0.7008\n",
      "has spend time 80m 52s/n\n",
      "val Loss: 0.5563 Acc: 0.6993\n",
      "has spend time 80m 53s/n\n",
      "\n",
      "Epoch 2275/9999\n",
      "----------\n",
      "train Loss: 0.5094 Acc: 0.7418\n",
      "has spend time 80m 54s/n\n",
      "val Loss: 0.5475 Acc: 0.7190\n",
      "has spend time 80m 55s/n\n",
      "\n",
      "Epoch 2276/9999\n",
      "----------\n",
      "train Loss: 0.5268 Acc: 0.7295\n",
      "has spend time 80m 56s/n\n",
      "val Loss: 0.5445 Acc: 0.7190\n",
      "has spend time 80m 57s/n\n",
      "\n",
      "Epoch 2277/9999\n",
      "----------\n",
      "train Loss: 0.5188 Acc: 0.7664\n",
      "has spend time 80m 59s/n\n",
      "val Loss: 0.5578 Acc: 0.6928\n",
      "has spend time 80m 59s/n\n",
      "\n",
      "Epoch 2278/9999\n",
      "----------\n",
      "train Loss: 0.4865 Acc: 0.7541\n",
      "has spend time 81m 1s/n\n",
      "val Loss: 0.5563 Acc: 0.6928\n",
      "has spend time 81m 2s/n\n",
      "\n",
      "Epoch 2279/9999\n",
      "----------\n",
      "train Loss: 0.5211 Acc: 0.7336\n",
      "has spend time 81m 3s/n\n",
      "val Loss: 0.5549 Acc: 0.7059\n",
      "has spend time 81m 4s/n\n",
      "\n",
      "Epoch 2280/9999\n",
      "----------\n",
      "train Loss: 0.5303 Acc: 0.7090\n",
      "has spend time 81m 5s/n\n",
      "val Loss: 0.5534 Acc: 0.7059\n",
      "has spend time 81m 6s/n\n",
      "\n",
      "Epoch 2281/9999\n",
      "----------\n",
      "train Loss: 0.4841 Acc: 0.7541\n",
      "has spend time 81m 7s/n\n",
      "val Loss: 0.5609 Acc: 0.6993\n",
      "has spend time 81m 8s/n\n",
      "\n",
      "Epoch 2282/9999\n",
      "----------\n",
      "train Loss: 0.5078 Acc: 0.7377\n",
      "has spend time 81m 9s/n\n",
      "val Loss: 0.5663 Acc: 0.6993\n",
      "has spend time 81m 10s/n\n",
      "\n",
      "Epoch 2283/9999\n",
      "----------\n",
      "train Loss: 0.5292 Acc: 0.7295\n",
      "has spend time 81m 11s/n\n",
      "val Loss: 0.5559 Acc: 0.7059\n",
      "has spend time 81m 12s/n\n",
      "\n",
      "Epoch 2284/9999\n",
      "----------\n",
      "train Loss: 0.5197 Acc: 0.7459\n",
      "has spend time 81m 13s/n\n",
      "val Loss: 0.5509 Acc: 0.7059\n",
      "has spend time 81m 14s/n\n",
      "\n",
      "Epoch 2285/9999\n",
      "----------\n",
      "train Loss: 0.4923 Acc: 0.7336\n",
      "has spend time 81m 15s/n\n",
      "val Loss: 0.5590 Acc: 0.6993\n",
      "has spend time 81m 16s/n\n",
      "\n",
      "Epoch 2286/9999\n",
      "----------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train Loss: 0.5230 Acc: 0.7131\n",
      "has spend time 81m 17s/n\n",
      "val Loss: 0.5467 Acc: 0.7190\n",
      "has spend time 81m 18s/n\n",
      "\n",
      "Epoch 2287/9999\n",
      "----------\n",
      "train Loss: 0.5251 Acc: 0.7459\n",
      "has spend time 81m 19s/n\n",
      "val Loss: 0.5518 Acc: 0.7124\n",
      "has spend time 81m 20s/n\n",
      "\n",
      "Epoch 2288/9999\n",
      "----------\n",
      "train Loss: 0.4966 Acc: 0.7459\n",
      "has spend time 81m 22s/n\n",
      "val Loss: 0.5522 Acc: 0.7059\n",
      "has spend time 81m 22s/n\n",
      "\n",
      "Epoch 2289/9999\n",
      "----------\n",
      "train Loss: 0.4923 Acc: 0.7336\n",
      "has spend time 81m 24s/n\n",
      "val Loss: 0.5463 Acc: 0.7059\n",
      "has spend time 81m 25s/n\n",
      "\n",
      "Epoch 2290/9999\n",
      "----------\n",
      "train Loss: 0.4830 Acc: 0.7500\n",
      "has spend time 81m 26s/n\n",
      "val Loss: 0.5495 Acc: 0.7124\n",
      "has spend time 81m 27s/n\n",
      "\n",
      "Epoch 2291/9999\n",
      "----------\n",
      "train Loss: 0.5273 Acc: 0.7336\n",
      "has spend time 81m 28s/n\n",
      "val Loss: 0.5528 Acc: 0.6863\n",
      "has spend time 81m 29s/n\n",
      "\n",
      "Epoch 2292/9999\n",
      "----------\n",
      "train Loss: 0.5202 Acc: 0.7172\n",
      "has spend time 81m 30s/n\n",
      "val Loss: 0.5431 Acc: 0.7124\n",
      "has spend time 81m 31s/n\n",
      "\n",
      "Epoch 2293/9999\n",
      "----------\n",
      "train Loss: 0.5275 Acc: 0.7336\n",
      "has spend time 81m 32s/n\n",
      "val Loss: 0.5547 Acc: 0.7124\n",
      "has spend time 81m 33s/n\n",
      "\n",
      "Epoch 2294/9999\n",
      "----------\n",
      "train Loss: 0.5019 Acc: 0.7254\n",
      "has spend time 81m 34s/n\n",
      "val Loss: 0.5586 Acc: 0.7190\n",
      "has spend time 81m 35s/n\n",
      "\n",
      "Epoch 2295/9999\n",
      "----------\n",
      "train Loss: 0.5129 Acc: 0.7295\n",
      "has spend time 81m 36s/n\n",
      "val Loss: 0.5679 Acc: 0.6993\n",
      "has spend time 81m 37s/n\n",
      "\n",
      "Epoch 2296/9999\n",
      "----------\n",
      "train Loss: 0.5110 Acc: 0.7172\n",
      "has spend time 81m 39s/n\n",
      "val Loss: 0.5483 Acc: 0.7124\n",
      "has spend time 81m 40s/n\n",
      "\n",
      "Epoch 2297/9999\n",
      "----------\n",
      "train Loss: 0.5173 Acc: 0.7336\n",
      "has spend time 81m 41s/n\n",
      "val Loss: 0.5559 Acc: 0.6993\n",
      "has spend time 81m 42s/n\n",
      "\n",
      "Epoch 2298/9999\n",
      "----------\n",
      "train Loss: 0.4943 Acc: 0.7336\n",
      "has spend time 81m 43s/n\n",
      "val Loss: 0.5710 Acc: 0.6928\n",
      "has spend time 81m 44s/n\n",
      "\n",
      "Epoch 2299/9999\n",
      "----------\n",
      "train Loss: 0.4912 Acc: 0.7541\n",
      "has spend time 81m 45s/n\n",
      "val Loss: 0.5571 Acc: 0.7059\n",
      "has spend time 81m 46s/n\n",
      "\n",
      "Epoch 2300/9999\n",
      "----------\n",
      "train Loss: 0.5096 Acc: 0.7541\n",
      "has spend time 81m 47s/n\n",
      "val Loss: 0.5555 Acc: 0.7059\n",
      "has spend time 81m 48s/n\n",
      "\n",
      "Epoch 2301/9999\n",
      "----------\n",
      "train Loss: 0.5001 Acc: 0.7295\n",
      "has spend time 81m 49s/n\n",
      "val Loss: 0.5718 Acc: 0.6993\n",
      "has spend time 81m 50s/n\n",
      "\n",
      "Epoch 2302/9999\n",
      "----------\n",
      "train Loss: 0.5366 Acc: 0.7090\n",
      "has spend time 81m 51s/n\n",
      "val Loss: 0.5509 Acc: 0.6993\n",
      "has spend time 81m 52s/n\n",
      "\n",
      "Epoch 2303/9999\n",
      "----------\n",
      "train Loss: 0.5200 Acc: 0.7295\n",
      "has spend time 81m 53s/n\n",
      "val Loss: 0.5718 Acc: 0.6928\n",
      "has spend time 81m 54s/n\n",
      "\n",
      "Epoch 2304/9999\n",
      "----------\n",
      "train Loss: 0.5095 Acc: 0.7254\n",
      "has spend time 81m 55s/n\n",
      "val Loss: 0.5555 Acc: 0.6928\n",
      "has spend time 81m 56s/n\n",
      "\n",
      "Epoch 2305/9999\n",
      "----------\n",
      "train Loss: 0.5145 Acc: 0.7664\n",
      "has spend time 81m 58s/n\n",
      "val Loss: 0.5420 Acc: 0.7190\n",
      "has spend time 81m 58s/n\n",
      "\n",
      "Epoch 2306/9999\n",
      "----------\n",
      "train Loss: 0.5223 Acc: 0.7131\n",
      "has spend time 81m 60s/n\n",
      "val Loss: 0.5540 Acc: 0.6993\n",
      "has spend time 82m 0s/n\n",
      "\n",
      "Epoch 2307/9999\n",
      "----------\n",
      "train Loss: 0.5017 Acc: 0.7254\n",
      "has spend time 82m 2s/n\n",
      "val Loss: 0.5556 Acc: 0.6863\n",
      "has spend time 82m 3s/n\n",
      "\n",
      "Epoch 2308/9999\n",
      "----------\n",
      "train Loss: 0.5069 Acc: 0.7336\n",
      "has spend time 82m 4s/n\n",
      "val Loss: 0.5523 Acc: 0.6928\n",
      "has spend time 82m 5s/n\n",
      "\n",
      "Epoch 2309/9999\n",
      "----------\n",
      "train Loss: 0.4917 Acc: 0.7336\n",
      "has spend time 82m 7s/n\n",
      "val Loss: 0.5440 Acc: 0.7059\n",
      "has spend time 82m 7s/n\n",
      "\n",
      "Epoch 2310/9999\n",
      "----------\n",
      "train Loss: 0.4883 Acc: 0.7664\n",
      "has spend time 82m 9s/n\n",
      "val Loss: 0.5626 Acc: 0.6993\n",
      "has spend time 82m 9s/n\n",
      "\n",
      "Epoch 2311/9999\n",
      "----------\n",
      "train Loss: 0.5258 Acc: 0.7336\n",
      "has spend time 82m 11s/n\n",
      "val Loss: 0.5604 Acc: 0.6928\n",
      "has spend time 82m 11s/n\n",
      "\n",
      "Epoch 2312/9999\n",
      "----------\n",
      "train Loss: 0.5134 Acc: 0.7500\n",
      "has spend time 82m 13s/n\n",
      "val Loss: 0.5664 Acc: 0.6993\n",
      "has spend time 82m 13s/n\n",
      "\n",
      "Epoch 2313/9999\n",
      "----------\n",
      "train Loss: 0.4934 Acc: 0.7459\n",
      "has spend time 82m 15s/n\n",
      "val Loss: 0.5765 Acc: 0.6928\n",
      "has spend time 82m 16s/n\n",
      "\n",
      "Epoch 2314/9999\n",
      "----------\n",
      "train Loss: 0.5069 Acc: 0.7541\n",
      "has spend time 82m 17s/n\n",
      "val Loss: 0.5418 Acc: 0.7190\n",
      "has spend time 82m 18s/n\n",
      "\n",
      "Epoch 2315/9999\n",
      "----------\n",
      "train Loss: 0.5384 Acc: 0.6844\n",
      "has spend time 82m 20s/n\n",
      "val Loss: 0.5584 Acc: 0.7059\n",
      "has spend time 82m 20s/n\n",
      "\n",
      "Epoch 2316/9999\n",
      "----------\n",
      "train Loss: 0.5012 Acc: 0.7459\n",
      "has spend time 82m 22s/n\n",
      "val Loss: 0.5537 Acc: 0.7059\n",
      "has spend time 82m 23s/n\n",
      "\n",
      "Epoch 2317/9999\n",
      "----------\n",
      "train Loss: 0.5134 Acc: 0.7500\n",
      "has spend time 82m 24s/n\n",
      "val Loss: 0.5398 Acc: 0.7124\n",
      "has spend time 82m 25s/n\n",
      "\n",
      "Epoch 2318/9999\n",
      "----------\n",
      "train Loss: 0.5278 Acc: 0.7213\n",
      "has spend time 82m 26s/n\n",
      "val Loss: 0.5529 Acc: 0.7124\n",
      "has spend time 82m 27s/n\n",
      "\n",
      "Epoch 2319/9999\n",
      "----------\n",
      "train Loss: 0.4896 Acc: 0.7418\n",
      "has spend time 82m 28s/n\n",
      "val Loss: 0.5486 Acc: 0.7190\n",
      "has spend time 82m 29s/n\n",
      "\n",
      "Epoch 2320/9999\n",
      "----------\n",
      "train Loss: 0.5291 Acc: 0.7172\n",
      "has spend time 82m 30s/n\n",
      "val Loss: 0.5426 Acc: 0.7255\n",
      "has spend time 82m 31s/n\n",
      "\n",
      "Epoch 2321/9999\n",
      "----------\n",
      "train Loss: 0.5020 Acc: 0.7377\n",
      "has spend time 82m 32s/n\n",
      "val Loss: 0.5510 Acc: 0.7124\n",
      "has spend time 82m 33s/n\n",
      "\n",
      "Epoch 2322/9999\n",
      "----------\n",
      "train Loss: 0.5136 Acc: 0.7295\n",
      "has spend time 82m 35s/n\n",
      "val Loss: 0.5459 Acc: 0.7190\n",
      "has spend time 82m 36s/n\n",
      "\n",
      "Epoch 2323/9999\n",
      "----------\n",
      "train Loss: 0.5256 Acc: 0.7172\n",
      "has spend time 82m 37s/n\n",
      "val Loss: 0.5529 Acc: 0.7059\n",
      "has spend time 82m 38s/n\n",
      "\n",
      "Epoch 2324/9999\n",
      "----------\n",
      "train Loss: 0.5084 Acc: 0.7418\n",
      "has spend time 82m 39s/n\n",
      "val Loss: 0.5493 Acc: 0.7190\n",
      "has spend time 82m 40s/n\n",
      "\n",
      "Epoch 2325/9999\n",
      "----------\n",
      "train Loss: 0.5090 Acc: 0.7377\n",
      "has spend time 82m 41s/n\n",
      "val Loss: 0.5509 Acc: 0.7059\n",
      "has spend time 82m 42s/n\n",
      "\n",
      "Epoch 2326/9999\n",
      "----------\n",
      "train Loss: 0.4969 Acc: 0.7746\n",
      "has spend time 82m 43s/n\n",
      "val Loss: 0.5540 Acc: 0.7059\n",
      "has spend time 82m 44s/n\n",
      "\n",
      "Epoch 2327/9999\n",
      "----------\n",
      "train Loss: 0.5062 Acc: 0.7664\n",
      "has spend time 82m 45s/n\n",
      "val Loss: 0.5495 Acc: 0.7124\n",
      "has spend time 82m 46s/n\n",
      "\n",
      "Epoch 2328/9999\n",
      "----------\n",
      "train Loss: 0.4993 Acc: 0.7377\n",
      "has spend time 82m 48s/n\n",
      "val Loss: 0.5550 Acc: 0.7059\n",
      "has spend time 82m 49s/n\n",
      "\n",
      "Epoch 2329/9999\n",
      "----------\n",
      "train Loss: 0.4835 Acc: 0.7746\n",
      "has spend time 82m 50s/n\n",
      "val Loss: 0.5473 Acc: 0.7124\n",
      "has spend time 82m 51s/n\n",
      "\n",
      "Epoch 2330/9999\n",
      "----------\n",
      "train Loss: 0.4970 Acc: 0.7049\n",
      "has spend time 82m 52s/n\n",
      "val Loss: 0.5523 Acc: 0.7124\n",
      "has spend time 82m 53s/n\n",
      "\n",
      "Epoch 2331/9999\n",
      "----------\n",
      "train Loss: 0.5436 Acc: 0.7213\n",
      "has spend time 82m 54s/n\n",
      "val Loss: 0.5476 Acc: 0.7124\n",
      "has spend time 82m 55s/n\n",
      "\n",
      "Epoch 2332/9999\n",
      "----------\n",
      "train Loss: 0.5337 Acc: 0.7254\n",
      "has spend time 82m 57s/n\n",
      "val Loss: 0.5518 Acc: 0.7059\n",
      "has spend time 82m 57s/n\n",
      "\n",
      "Epoch 2333/9999\n",
      "----------\n",
      "train Loss: 0.4850 Acc: 0.7828\n",
      "has spend time 82m 59s/n\n",
      "val Loss: 0.5489 Acc: 0.7059\n",
      "has spend time 82m 60s/n\n",
      "\n",
      "Epoch 2334/9999\n",
      "----------\n",
      "train Loss: 0.5312 Acc: 0.7213\n",
      "has spend time 83m 1s/n\n",
      "val Loss: 0.5419 Acc: 0.7190\n",
      "has spend time 83m 2s/n\n",
      "\n",
      "Epoch 2335/9999\n",
      "----------\n",
      "train Loss: 0.4986 Acc: 0.7541\n",
      "has spend time 83m 3s/n\n",
      "val Loss: 0.5573 Acc: 0.6993\n",
      "has spend time 83m 4s/n\n",
      "\n",
      "Epoch 2336/9999\n",
      "----------\n",
      "train Loss: 0.5280 Acc: 0.7131\n",
      "has spend time 83m 5s/n\n",
      "val Loss: 0.5497 Acc: 0.7059\n",
      "has spend time 83m 6s/n\n",
      "\n",
      "Epoch 2337/9999\n",
      "----------\n",
      "train Loss: 0.5109 Acc: 0.7172\n",
      "has spend time 83m 8s/n\n",
      "val Loss: 0.5541 Acc: 0.6993\n",
      "has spend time 83m 8s/n\n",
      "\n",
      "Epoch 2338/9999\n",
      "----------\n",
      "train Loss: 0.5106 Acc: 0.7336\n",
      "has spend time 83m 10s/n\n",
      "val Loss: 0.5470 Acc: 0.7190\n",
      "has spend time 83m 10s/n\n",
      "\n",
      "Epoch 2339/9999\n",
      "----------\n",
      "train Loss: 0.5032 Acc: 0.7295\n",
      "has spend time 83m 12s/n\n",
      "val Loss: 0.5490 Acc: 0.7124\n",
      "has spend time 83m 13s/n\n",
      "\n",
      "Epoch 2340/9999\n",
      "----------\n",
      "train Loss: 0.4698 Acc: 0.7705\n",
      "has spend time 83m 14s/n\n",
      "val Loss: 0.5513 Acc: 0.7059\n",
      "has spend time 83m 15s/n\n",
      "\n",
      "Epoch 2341/9999\n",
      "----------\n",
      "train Loss: 0.5050 Acc: 0.7295\n",
      "has spend time 83m 16s/n\n",
      "val Loss: 0.5508 Acc: 0.7059\n",
      "has spend time 83m 17s/n\n",
      "\n",
      "Epoch 2342/9999\n",
      "----------\n",
      "train Loss: 0.5164 Acc: 0.7459\n",
      "has spend time 83m 18s/n\n",
      "val Loss: 0.5406 Acc: 0.7255\n",
      "has spend time 83m 19s/n\n",
      "\n",
      "Epoch 2343/9999\n",
      "----------\n",
      "train Loss: 0.5060 Acc: 0.7295\n",
      "has spend time 83m 21s/n\n",
      "val Loss: 0.5430 Acc: 0.7255\n",
      "has spend time 83m 21s/n\n",
      "\n",
      "Epoch 2344/9999\n",
      "----------\n",
      "train Loss: 0.5057 Acc: 0.7295\n",
      "has spend time 83m 23s/n\n",
      "val Loss: 0.5452 Acc: 0.7059\n",
      "has spend time 83m 23s/n\n",
      "\n",
      "Epoch 2345/9999\n",
      "----------\n",
      "train Loss: 0.5040 Acc: 0.7541\n",
      "has spend time 83m 25s/n\n",
      "val Loss: 0.5491 Acc: 0.7190\n",
      "has spend time 83m 25s/n\n",
      "\n",
      "Epoch 2346/9999\n",
      "----------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train Loss: 0.5223 Acc: 0.7131\n",
      "has spend time 83m 27s/n\n",
      "val Loss: 0.5488 Acc: 0.7059\n",
      "has spend time 83m 27s/n\n",
      "\n",
      "Epoch 2347/9999\n",
      "----------\n",
      "train Loss: 0.5284 Acc: 0.7213\n",
      "has spend time 83m 29s/n\n",
      "val Loss: 0.5379 Acc: 0.7124\n",
      "has spend time 83m 30s/n\n",
      "\n",
      "Epoch 2348/9999\n",
      "----------\n",
      "train Loss: 0.5191 Acc: 0.7008\n",
      "has spend time 83m 31s/n\n",
      "val Loss: 0.5497 Acc: 0.7124\n",
      "has spend time 83m 32s/n\n",
      "\n",
      "Epoch 2349/9999\n",
      "----------\n",
      "train Loss: 0.5140 Acc: 0.7254\n",
      "has spend time 83m 33s/n\n",
      "val Loss: 0.5586 Acc: 0.6993\n",
      "has spend time 83m 34s/n\n",
      "\n",
      "Epoch 2350/9999\n",
      "----------\n",
      "train Loss: 0.4866 Acc: 0.7705\n",
      "has spend time 83m 35s/n\n",
      "val Loss: 0.5624 Acc: 0.6993\n",
      "has spend time 83m 36s/n\n",
      "\n",
      "Epoch 2351/9999\n",
      "----------\n",
      "train Loss: 0.5071 Acc: 0.7459\n",
      "has spend time 83m 37s/n\n",
      "val Loss: 0.5475 Acc: 0.7124\n",
      "has spend time 83m 38s/n\n",
      "\n",
      "Epoch 2352/9999\n",
      "----------\n",
      "train Loss: 0.4977 Acc: 0.7049\n",
      "has spend time 83m 40s/n\n",
      "val Loss: 0.5425 Acc: 0.7190\n",
      "has spend time 83m 41s/n\n",
      "\n",
      "Epoch 2353/9999\n",
      "----------\n",
      "train Loss: 0.5391 Acc: 0.7049\n",
      "has spend time 83m 42s/n\n",
      "val Loss: 0.5680 Acc: 0.6928\n",
      "has spend time 83m 43s/n\n",
      "\n",
      "Epoch 2354/9999\n",
      "----------\n",
      "train Loss: 0.4852 Acc: 0.7582\n",
      "has spend time 83m 44s/n\n",
      "val Loss: 0.5497 Acc: 0.7190\n",
      "has spend time 83m 45s/n\n",
      "\n",
      "Epoch 2355/9999\n",
      "----------\n",
      "train Loss: 0.5188 Acc: 0.7131\n",
      "has spend time 83m 46s/n\n",
      "val Loss: 0.5521 Acc: 0.7059\n",
      "has spend time 83m 47s/n\n",
      "\n",
      "Epoch 2356/9999\n",
      "----------\n",
      "train Loss: 0.5076 Acc: 0.7418\n",
      "has spend time 83m 48s/n\n",
      "val Loss: 0.5527 Acc: 0.7059\n",
      "has spend time 83m 49s/n\n",
      "\n",
      "Epoch 2357/9999\n",
      "----------\n",
      "train Loss: 0.4632 Acc: 0.7910\n",
      "has spend time 83m 50s/n\n",
      "val Loss: 0.5464 Acc: 0.7124\n",
      "has spend time 83m 51s/n\n",
      "\n",
      "Epoch 2358/9999\n",
      "----------\n",
      "train Loss: 0.4853 Acc: 0.7705\n",
      "has spend time 83m 52s/n\n",
      "val Loss: 0.5541 Acc: 0.6993\n",
      "has spend time 83m 53s/n\n",
      "\n",
      "Epoch 2359/9999\n",
      "----------\n",
      "train Loss: 0.5186 Acc: 0.6967\n",
      "has spend time 83m 55s/n\n",
      "val Loss: 0.5630 Acc: 0.6993\n",
      "has spend time 83m 55s/n\n",
      "\n",
      "Epoch 2360/9999\n",
      "----------\n",
      "train Loss: 0.5255 Acc: 0.7336\n",
      "has spend time 83m 57s/n\n",
      "val Loss: 0.5542 Acc: 0.7190\n",
      "has spend time 83m 57s/n\n",
      "\n",
      "Epoch 2361/9999\n",
      "----------\n",
      "train Loss: 0.5181 Acc: 0.7295\n",
      "has spend time 83m 59s/n\n",
      "val Loss: 0.5459 Acc: 0.7190\n",
      "has spend time 83m 59s/n\n",
      "\n",
      "Epoch 2362/9999\n",
      "----------\n",
      "train Loss: 0.4803 Acc: 0.7746\n",
      "has spend time 84m 1s/n\n",
      "val Loss: 0.5448 Acc: 0.7255\n",
      "has spend time 84m 1s/n\n",
      "\n",
      "Epoch 2363/9999\n",
      "----------\n",
      "train Loss: 0.5167 Acc: 0.7254\n",
      "has spend time 84m 3s/n\n",
      "val Loss: 0.5491 Acc: 0.7124\n",
      "has spend time 84m 3s/n\n",
      "\n",
      "Epoch 2364/9999\n",
      "----------\n",
      "train Loss: 0.5039 Acc: 0.7500\n",
      "has spend time 84m 5s/n\n",
      "val Loss: 0.5441 Acc: 0.7124\n",
      "has spend time 84m 6s/n\n",
      "\n",
      "Epoch 2365/9999\n",
      "----------\n",
      "train Loss: 0.5047 Acc: 0.7541\n",
      "has spend time 84m 7s/n\n",
      "val Loss: 0.5479 Acc: 0.7124\n",
      "has spend time 84m 8s/n\n",
      "\n",
      "Epoch 2366/9999\n",
      "----------\n",
      "train Loss: 0.5091 Acc: 0.7213\n",
      "has spend time 84m 9s/n\n",
      "val Loss: 0.5568 Acc: 0.7059\n",
      "has spend time 84m 10s/n\n",
      "\n",
      "Epoch 2367/9999\n",
      "----------\n",
      "train Loss: 0.5089 Acc: 0.7541\n",
      "has spend time 84m 11s/n\n",
      "val Loss: 0.5446 Acc: 0.7190\n",
      "has spend time 84m 12s/n\n",
      "\n",
      "Epoch 2368/9999\n",
      "----------\n",
      "train Loss: 0.5381 Acc: 0.7090\n",
      "has spend time 84m 14s/n\n",
      "val Loss: 0.5502 Acc: 0.7059\n",
      "has spend time 84m 15s/n\n",
      "\n",
      "Epoch 2369/9999\n",
      "----------\n",
      "train Loss: 0.5068 Acc: 0.7459\n",
      "has spend time 84m 16s/n\n",
      "val Loss: 0.5541 Acc: 0.7059\n",
      "has spend time 84m 17s/n\n",
      "\n",
      "Epoch 2370/9999\n",
      "----------\n",
      "train Loss: 0.5055 Acc: 0.7254\n",
      "has spend time 84m 19s/n\n",
      "val Loss: 0.5481 Acc: 0.7124\n",
      "has spend time 84m 19s/n\n",
      "\n",
      "Epoch 2371/9999\n",
      "----------\n",
      "train Loss: 0.5180 Acc: 0.7090\n",
      "has spend time 84m 21s/n\n",
      "val Loss: 0.5407 Acc: 0.7190\n",
      "has spend time 84m 21s/n\n",
      "\n",
      "Epoch 2372/9999\n",
      "----------\n",
      "train Loss: 0.5022 Acc: 0.7213\n",
      "has spend time 84m 23s/n\n",
      "val Loss: 0.5437 Acc: 0.7190\n",
      "has spend time 84m 23s/n\n",
      "\n",
      "Epoch 2373/9999\n",
      "----------\n",
      "train Loss: 0.5047 Acc: 0.7049\n",
      "has spend time 84m 25s/n\n",
      "val Loss: 0.5470 Acc: 0.7190\n",
      "has spend time 84m 25s/n\n",
      "\n",
      "Epoch 2374/9999\n",
      "----------\n",
      "train Loss: 0.5054 Acc: 0.7377\n",
      "has spend time 84m 27s/n\n",
      "val Loss: 0.5519 Acc: 0.7124\n",
      "has spend time 84m 27s/n\n",
      "\n",
      "Epoch 2375/9999\n",
      "----------\n",
      "train Loss: 0.4996 Acc: 0.7541\n",
      "has spend time 84m 29s/n\n",
      "val Loss: 0.5516 Acc: 0.6993\n",
      "has spend time 84m 30s/n\n",
      "\n",
      "Epoch 2376/9999\n",
      "----------\n",
      "train Loss: 0.5237 Acc: 0.7377\n",
      "has spend time 84m 31s/n\n",
      "val Loss: 0.5451 Acc: 0.7190\n",
      "has spend time 84m 32s/n\n",
      "\n",
      "Epoch 2377/9999\n",
      "----------\n",
      "train Loss: 0.4779 Acc: 0.7664\n",
      "has spend time 84m 34s/n\n",
      "val Loss: 0.5529 Acc: 0.7124\n",
      "has spend time 84m 34s/n\n",
      "\n",
      "Epoch 2378/9999\n",
      "----------\n",
      "train Loss: 0.5339 Acc: 0.7377\n",
      "has spend time 84m 36s/n\n",
      "val Loss: 0.5629 Acc: 0.6928\n",
      "has spend time 84m 36s/n\n",
      "\n",
      "Epoch 2379/9999\n",
      "----------\n",
      "train Loss: 0.5139 Acc: 0.7295\n",
      "has spend time 84m 38s/n\n",
      "val Loss: 0.5491 Acc: 0.7059\n",
      "has spend time 84m 38s/n\n",
      "\n",
      "Epoch 2380/9999\n",
      "----------\n",
      "train Loss: 0.5207 Acc: 0.7582\n",
      "has spend time 84m 40s/n\n",
      "val Loss: 0.5518 Acc: 0.7124\n",
      "has spend time 84m 40s/n\n",
      "\n",
      "Epoch 2381/9999\n",
      "----------\n",
      "train Loss: 0.5018 Acc: 0.7418\n",
      "has spend time 84m 42s/n\n",
      "val Loss: 0.5502 Acc: 0.7059\n",
      "has spend time 84m 42s/n\n",
      "\n",
      "Epoch 2382/9999\n",
      "----------\n",
      "train Loss: 0.5261 Acc: 0.7336\n",
      "has spend time 84m 44s/n\n",
      "val Loss: 0.5601 Acc: 0.6928\n",
      "has spend time 84m 44s/n\n",
      "\n",
      "Epoch 2383/9999\n",
      "----------\n",
      "train Loss: 0.5164 Acc: 0.7459\n",
      "has spend time 84m 46s/n\n",
      "val Loss: 0.5480 Acc: 0.7059\n",
      "has spend time 84m 46s/n\n",
      "\n",
      "Epoch 2384/9999\n",
      "----------\n",
      "train Loss: 0.5124 Acc: 0.7254\n",
      "has spend time 84m 48s/n\n",
      "val Loss: 0.5499 Acc: 0.7059\n",
      "has spend time 84m 49s/n\n",
      "\n",
      "Epoch 2385/9999\n",
      "----------\n",
      "train Loss: 0.4992 Acc: 0.7459\n",
      "has spend time 84m 50s/n\n",
      "val Loss: 0.5445 Acc: 0.7124\n",
      "has spend time 84m 51s/n\n",
      "\n",
      "Epoch 2386/9999\n",
      "----------\n",
      "train Loss: 0.5225 Acc: 0.7172\n",
      "has spend time 84m 52s/n\n",
      "val Loss: 0.5432 Acc: 0.7255\n",
      "has spend time 84m 53s/n\n",
      "\n",
      "Epoch 2387/9999\n",
      "----------\n",
      "train Loss: 0.5195 Acc: 0.7377\n",
      "has spend time 84m 55s/n\n",
      "val Loss: 0.5415 Acc: 0.7124\n",
      "has spend time 84m 55s/n\n",
      "\n",
      "Epoch 2388/9999\n",
      "----------\n",
      "train Loss: 0.5066 Acc: 0.7131\n",
      "has spend time 84m 57s/n\n",
      "val Loss: 0.5524 Acc: 0.7059\n",
      "has spend time 84m 57s/n\n",
      "\n",
      "Epoch 2389/9999\n",
      "----------\n",
      "train Loss: 0.4601 Acc: 0.7623\n",
      "has spend time 84m 59s/n\n",
      "val Loss: 0.5687 Acc: 0.6928\n",
      "has spend time 84m 59s/n\n",
      "\n",
      "Epoch 2390/9999\n",
      "----------\n",
      "train Loss: 0.5134 Acc: 0.7336\n",
      "has spend time 85m 1s/n\n",
      "val Loss: 0.5454 Acc: 0.7124\n",
      "has spend time 85m 1s/n\n",
      "\n",
      "Epoch 2391/9999\n",
      "----------\n",
      "train Loss: 0.5205 Acc: 0.7418\n",
      "has spend time 85m 3s/n\n",
      "val Loss: 0.5526 Acc: 0.6993\n",
      "has spend time 85m 4s/n\n",
      "\n",
      "Epoch 2392/9999\n",
      "----------\n",
      "train Loss: 0.4904 Acc: 0.7582\n",
      "has spend time 85m 5s/n\n",
      "val Loss: 0.5544 Acc: 0.6993\n",
      "has spend time 85m 6s/n\n",
      "\n",
      "Epoch 2393/9999\n",
      "----------\n",
      "train Loss: 0.5085 Acc: 0.7377\n",
      "has spend time 85m 8s/n\n",
      "val Loss: 0.5489 Acc: 0.6993\n",
      "has spend time 85m 8s/n\n",
      "\n",
      "Epoch 2394/9999\n",
      "----------\n",
      "train Loss: 0.5026 Acc: 0.7623\n",
      "has spend time 85m 10s/n\n",
      "val Loss: 0.5522 Acc: 0.7190\n",
      "has spend time 85m 10s/n\n",
      "\n",
      "Epoch 2395/9999\n",
      "----------\n",
      "train Loss: 0.5193 Acc: 0.7131\n",
      "has spend time 85m 12s/n\n",
      "val Loss: 0.5451 Acc: 0.7059\n",
      "has spend time 85m 12s/n\n",
      "\n",
      "Epoch 2396/9999\n",
      "----------\n",
      "train Loss: 0.5201 Acc: 0.7172\n",
      "has spend time 85m 14s/n\n",
      "val Loss: 0.5488 Acc: 0.7059\n",
      "has spend time 85m 14s/n\n",
      "\n",
      "Epoch 2397/9999\n",
      "----------\n",
      "train Loss: 0.5224 Acc: 0.7008\n",
      "has spend time 85m 16s/n\n",
      "val Loss: 0.5481 Acc: 0.7190\n",
      "has spend time 85m 16s/n\n",
      "\n",
      "Epoch 2398/9999\n",
      "----------\n",
      "train Loss: 0.5152 Acc: 0.7131\n",
      "has spend time 85m 18s/n\n",
      "val Loss: 0.5396 Acc: 0.7124\n",
      "has spend time 85m 18s/n\n",
      "\n",
      "Epoch 2399/9999\n",
      "----------\n",
      "train Loss: 0.5050 Acc: 0.7213\n",
      "has spend time 85m 20s/n\n",
      "val Loss: 0.5469 Acc: 0.7124\n",
      "has spend time 85m 20s/n\n",
      "\n",
      "Epoch 2400/9999\n",
      "----------\n",
      "train Loss: 0.4945 Acc: 0.7787\n",
      "has spend time 85m 22s/n\n",
      "val Loss: 0.5492 Acc: 0.7190\n",
      "has spend time 85m 23s/n\n",
      "\n",
      "Epoch 2401/9999\n",
      "----------\n",
      "train Loss: 0.4940 Acc: 0.7377\n",
      "has spend time 85m 24s/n\n",
      "val Loss: 0.5513 Acc: 0.7124\n",
      "has spend time 85m 25s/n\n",
      "\n",
      "Epoch 2402/9999\n",
      "----------\n",
      "train Loss: 0.5046 Acc: 0.7377\n",
      "has spend time 85m 26s/n\n",
      "val Loss: 0.5515 Acc: 0.6993\n",
      "has spend time 85m 27s/n\n",
      "\n",
      "Epoch 2403/9999\n",
      "----------\n",
      "train Loss: 0.4849 Acc: 0.7705\n",
      "has spend time 85m 28s/n\n",
      "val Loss: 0.5451 Acc: 0.7124\n",
      "has spend time 85m 29s/n\n",
      "\n",
      "Epoch 2404/9999\n",
      "----------\n",
      "train Loss: 0.5322 Acc: 0.7131\n",
      "has spend time 85m 30s/n\n",
      "val Loss: 0.5485 Acc: 0.7124\n",
      "has spend time 85m 31s/n\n",
      "\n",
      "Epoch 2405/9999\n",
      "----------\n",
      "train Loss: 0.4991 Acc: 0.7500\n",
      "has spend time 85m 33s/n\n",
      "val Loss: 0.5570 Acc: 0.7059\n",
      "has spend time 85m 33s/n\n",
      "\n",
      "Epoch 2406/9999\n",
      "----------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train Loss: 0.5104 Acc: 0.7500\n",
      "has spend time 85m 35s/n\n",
      "val Loss: 0.5451 Acc: 0.7124\n",
      "has spend time 85m 35s/n\n",
      "\n",
      "Epoch 2407/9999\n",
      "----------\n",
      "train Loss: 0.5076 Acc: 0.7336\n",
      "has spend time 85m 37s/n\n",
      "val Loss: 0.5432 Acc: 0.7190\n",
      "has spend time 85m 38s/n\n",
      "\n",
      "Epoch 2408/9999\n",
      "----------\n",
      "train Loss: 0.5133 Acc: 0.7541\n",
      "has spend time 85m 39s/n\n",
      "val Loss: 0.5527 Acc: 0.7124\n",
      "has spend time 85m 40s/n\n",
      "\n",
      "Epoch 2409/9999\n",
      "----------\n",
      "train Loss: 0.5228 Acc: 0.7295\n",
      "has spend time 85m 41s/n\n",
      "val Loss: 0.5493 Acc: 0.7124\n",
      "has spend time 85m 42s/n\n",
      "\n",
      "Epoch 2410/9999\n",
      "----------\n",
      "train Loss: 0.4803 Acc: 0.7623\n",
      "has spend time 85m 43s/n\n",
      "val Loss: 0.5456 Acc: 0.7320\n",
      "has spend time 85m 44s/n\n",
      "\n",
      "Epoch 2411/9999\n",
      "----------\n",
      "train Loss: 0.5285 Acc: 0.7295\n",
      "has spend time 85m 45s/n\n",
      "val Loss: 0.5463 Acc: 0.7190\n",
      "has spend time 85m 46s/n\n",
      "\n",
      "Epoch 2412/9999\n",
      "----------\n",
      "train Loss: 0.4708 Acc: 0.8033\n",
      "has spend time 85m 47s/n\n",
      "val Loss: 0.5560 Acc: 0.7059\n",
      "has spend time 85m 48s/n\n",
      "\n",
      "Epoch 2413/9999\n",
      "----------\n",
      "train Loss: 0.5077 Acc: 0.7377\n",
      "has spend time 85m 49s/n\n",
      "val Loss: 0.5544 Acc: 0.7124\n",
      "has spend time 85m 50s/n\n",
      "\n",
      "Epoch 2414/9999\n",
      "----------\n",
      "train Loss: 0.5046 Acc: 0.7582\n",
      "has spend time 85m 51s/n\n",
      "val Loss: 0.5423 Acc: 0.7059\n",
      "has spend time 85m 52s/n\n",
      "\n",
      "Epoch 2415/9999\n",
      "----------\n",
      "train Loss: 0.5014 Acc: 0.7377\n",
      "has spend time 85m 53s/n\n",
      "val Loss: 0.5445 Acc: 0.7124\n",
      "has spend time 85m 54s/n\n",
      "\n",
      "Epoch 2416/9999\n",
      "----------\n",
      "train Loss: 0.5197 Acc: 0.7377\n",
      "has spend time 85m 56s/n\n",
      "val Loss: 0.5517 Acc: 0.6993\n",
      "has spend time 85m 56s/n\n",
      "\n",
      "Epoch 2417/9999\n",
      "----------\n",
      "train Loss: 0.5344 Acc: 0.7295\n",
      "has spend time 85m 58s/n\n",
      "val Loss: 0.5569 Acc: 0.6993\n",
      "has spend time 85m 58s/n\n",
      "\n",
      "Epoch 2418/9999\n",
      "----------\n",
      "train Loss: 0.4811 Acc: 0.7336\n",
      "has spend time 85m 60s/n\n",
      "val Loss: 0.5401 Acc: 0.7124\n",
      "has spend time 86m 1s/n\n",
      "\n",
      "Epoch 2419/9999\n",
      "----------\n",
      "train Loss: 0.5114 Acc: 0.7295\n",
      "has spend time 86m 2s/n\n",
      "val Loss: 0.5495 Acc: 0.7124\n",
      "has spend time 86m 3s/n\n",
      "\n",
      "Epoch 2420/9999\n",
      "----------\n",
      "train Loss: 0.5048 Acc: 0.7582\n",
      "has spend time 86m 4s/n\n",
      "val Loss: 0.5477 Acc: 0.7059\n",
      "has spend time 86m 5s/n\n",
      "\n",
      "Epoch 2421/9999\n",
      "----------\n",
      "train Loss: 0.4997 Acc: 0.7213\n",
      "has spend time 86m 6s/n\n",
      "val Loss: 0.5490 Acc: 0.7059\n",
      "has spend time 86m 7s/n\n",
      "\n",
      "Epoch 2422/9999\n",
      "----------\n",
      "train Loss: 0.5235 Acc: 0.7254\n",
      "has spend time 86m 8s/n\n",
      "val Loss: 0.5617 Acc: 0.6928\n",
      "has spend time 86m 9s/n\n",
      "\n",
      "Epoch 2423/9999\n",
      "----------\n",
      "train Loss: 0.5055 Acc: 0.7377\n",
      "has spend time 86m 10s/n\n",
      "val Loss: 0.5552 Acc: 0.6993\n",
      "has spend time 86m 11s/n\n",
      "\n",
      "Epoch 2424/9999\n",
      "----------\n",
      "train Loss: 0.4902 Acc: 0.7582\n",
      "has spend time 86m 13s/n\n",
      "val Loss: 0.5549 Acc: 0.7059\n",
      "has spend time 86m 13s/n\n",
      "\n",
      "Epoch 2425/9999\n",
      "----------\n",
      "train Loss: 0.4859 Acc: 0.7828\n",
      "has spend time 86m 15s/n\n",
      "val Loss: 0.5446 Acc: 0.6993\n",
      "has spend time 86m 15s/n\n",
      "\n",
      "Epoch 2426/9999\n",
      "----------\n",
      "train Loss: 0.4957 Acc: 0.7377\n",
      "has spend time 86m 17s/n\n",
      "val Loss: 0.5548 Acc: 0.7059\n",
      "has spend time 86m 17s/n\n",
      "\n",
      "Epoch 2427/9999\n",
      "----------\n",
      "train Loss: 0.5098 Acc: 0.7500\n",
      "has spend time 86m 19s/n\n",
      "val Loss: 0.5631 Acc: 0.6928\n",
      "has spend time 86m 19s/n\n",
      "\n",
      "Epoch 2428/9999\n",
      "----------\n",
      "train Loss: 0.4947 Acc: 0.7541\n",
      "has spend time 86m 21s/n\n",
      "val Loss: 0.5438 Acc: 0.7255\n",
      "has spend time 86m 22s/n\n",
      "\n",
      "Epoch 2429/9999\n",
      "----------\n",
      "train Loss: 0.5410 Acc: 0.7336\n",
      "has spend time 86m 23s/n\n",
      "val Loss: 0.5464 Acc: 0.7124\n",
      "has spend time 86m 24s/n\n",
      "\n",
      "Epoch 2430/9999\n",
      "----------\n",
      "train Loss: 0.5016 Acc: 0.7623\n",
      "has spend time 86m 26s/n\n",
      "val Loss: 0.5597 Acc: 0.7059\n",
      "has spend time 86m 26s/n\n",
      "\n",
      "Epoch 2431/9999\n",
      "----------\n",
      "train Loss: 0.5172 Acc: 0.7172\n",
      "has spend time 86m 28s/n\n",
      "val Loss: 0.5511 Acc: 0.7190\n",
      "has spend time 86m 28s/n\n",
      "\n",
      "Epoch 2432/9999\n",
      "----------\n",
      "train Loss: 0.5349 Acc: 0.7336\n",
      "has spend time 86m 30s/n\n",
      "val Loss: 0.5483 Acc: 0.7124\n",
      "has spend time 86m 31s/n\n",
      "\n",
      "Epoch 2433/9999\n",
      "----------\n",
      "train Loss: 0.4921 Acc: 0.7459\n",
      "has spend time 86m 32s/n\n",
      "val Loss: 0.5443 Acc: 0.7124\n",
      "has spend time 86m 33s/n\n",
      "\n",
      "Epoch 2434/9999\n",
      "----------\n",
      "train Loss: 0.4985 Acc: 0.7582\n",
      "has spend time 86m 34s/n\n",
      "val Loss: 0.5483 Acc: 0.7059\n",
      "has spend time 86m 35s/n\n",
      "\n",
      "Epoch 2435/9999\n",
      "----------\n",
      "train Loss: 0.4932 Acc: 0.7664\n",
      "has spend time 86m 37s/n\n",
      "val Loss: 0.5450 Acc: 0.7124\n",
      "has spend time 86m 38s/n\n",
      "\n",
      "Epoch 2436/9999\n",
      "----------\n",
      "train Loss: 0.5027 Acc: 0.7254\n",
      "has spend time 86m 39s/n\n",
      "val Loss: 0.5453 Acc: 0.7124\n",
      "has spend time 86m 40s/n\n",
      "\n",
      "Epoch 2437/9999\n",
      "----------\n",
      "train Loss: 0.5019 Acc: 0.7418\n",
      "has spend time 86m 41s/n\n",
      "val Loss: 0.5638 Acc: 0.6928\n",
      "has spend time 86m 42s/n\n",
      "\n",
      "Epoch 2438/9999\n",
      "----------\n",
      "train Loss: 0.4973 Acc: 0.7459\n",
      "has spend time 86m 43s/n\n",
      "val Loss: 0.5613 Acc: 0.6993\n",
      "has spend time 86m 44s/n\n",
      "\n",
      "Epoch 2439/9999\n",
      "----------\n",
      "train Loss: 0.4975 Acc: 0.7377\n",
      "has spend time 86m 46s/n\n",
      "val Loss: 0.5527 Acc: 0.6928\n",
      "has spend time 86m 47s/n\n",
      "\n",
      "Epoch 2440/9999\n",
      "----------\n",
      "train Loss: 0.4981 Acc: 0.7377\n",
      "has spend time 86m 48s/n\n",
      "val Loss: 0.5491 Acc: 0.6993\n",
      "has spend time 86m 49s/n\n",
      "\n",
      "Epoch 2441/9999\n",
      "----------\n",
      "train Loss: 0.5058 Acc: 0.7377\n",
      "has spend time 86m 50s/n\n",
      "val Loss: 0.5460 Acc: 0.7190\n",
      "has spend time 86m 51s/n\n",
      "\n",
      "Epoch 2442/9999\n",
      "----------\n",
      "train Loss: 0.5009 Acc: 0.7623\n",
      "has spend time 86m 52s/n\n",
      "val Loss: 0.5638 Acc: 0.6928\n",
      "has spend time 86m 53s/n\n",
      "\n",
      "Epoch 2443/9999\n",
      "----------\n",
      "train Loss: 0.4930 Acc: 0.7459\n",
      "has spend time 86m 54s/n\n",
      "val Loss: 0.5504 Acc: 0.7059\n",
      "has spend time 86m 55s/n\n",
      "\n",
      "Epoch 2444/9999\n",
      "----------\n",
      "train Loss: 0.4669 Acc: 0.7705\n",
      "has spend time 86m 56s/n\n",
      "val Loss: 0.5513 Acc: 0.6993\n",
      "has spend time 86m 57s/n\n",
      "\n",
      "Epoch 2445/9999\n",
      "----------\n",
      "train Loss: 0.5202 Acc: 0.7131\n",
      "has spend time 86m 59s/n\n",
      "val Loss: 0.5602 Acc: 0.6863\n",
      "has spend time 86m 59s/n\n",
      "\n",
      "Epoch 2446/9999\n",
      "----------\n",
      "train Loss: 0.5071 Acc: 0.7459\n",
      "has spend time 87m 1s/n\n",
      "val Loss: 0.5509 Acc: 0.7059\n",
      "has spend time 87m 1s/n\n",
      "\n",
      "Epoch 2447/9999\n",
      "----------\n",
      "train Loss: 0.5205 Acc: 0.7008\n",
      "has spend time 87m 3s/n\n",
      "val Loss: 0.5732 Acc: 0.6797\n",
      "has spend time 87m 3s/n\n",
      "\n",
      "Epoch 2448/9999\n",
      "----------\n",
      "train Loss: 0.5164 Acc: 0.7295\n",
      "has spend time 87m 5s/n\n",
      "val Loss: 0.5594 Acc: 0.6993\n",
      "has spend time 87m 5s/n\n",
      "\n",
      "Epoch 2449/9999\n",
      "----------\n",
      "train Loss: 0.4936 Acc: 0.7336\n",
      "has spend time 87m 7s/n\n",
      "val Loss: 0.5574 Acc: 0.7059\n",
      "has spend time 87m 8s/n\n",
      "\n",
      "Epoch 2450/9999\n",
      "----------\n",
      "train Loss: 0.5047 Acc: 0.7459\n",
      "has spend time 87m 9s/n\n",
      "val Loss: 0.5401 Acc: 0.7124\n",
      "has spend time 87m 10s/n\n",
      "\n",
      "Epoch 2451/9999\n",
      "----------\n",
      "train Loss: 0.4801 Acc: 0.7500\n",
      "has spend time 87m 11s/n\n",
      "val Loss: 0.5437 Acc: 0.7190\n",
      "has spend time 87m 12s/n\n",
      "\n",
      "Epoch 2452/9999\n",
      "----------\n",
      "train Loss: 0.5088 Acc: 0.7582\n",
      "has spend time 87m 13s/n\n",
      "val Loss: 0.5519 Acc: 0.7059\n",
      "has spend time 87m 14s/n\n",
      "\n",
      "Epoch 2453/9999\n",
      "----------\n",
      "train Loss: 0.4934 Acc: 0.7254\n",
      "has spend time 87m 15s/n\n",
      "val Loss: 0.5488 Acc: 0.7059\n",
      "has spend time 87m 16s/n\n",
      "\n",
      "Epoch 2454/9999\n",
      "----------\n",
      "train Loss: 0.4862 Acc: 0.7459\n",
      "has spend time 87m 18s/n\n",
      "val Loss: 0.5469 Acc: 0.7059\n",
      "has spend time 87m 18s/n\n",
      "\n",
      "Epoch 2455/9999\n",
      "----------\n",
      "train Loss: 0.4791 Acc: 0.7664\n",
      "has spend time 87m 20s/n\n",
      "val Loss: 0.5496 Acc: 0.7059\n",
      "has spend time 87m 20s/n\n",
      "\n",
      "Epoch 2456/9999\n",
      "----------\n",
      "train Loss: 0.5093 Acc: 0.7541\n",
      "has spend time 87m 22s/n\n",
      "val Loss: 0.5611 Acc: 0.7059\n",
      "has spend time 87m 22s/n\n",
      "\n",
      "Epoch 2457/9999\n",
      "----------\n",
      "train Loss: 0.4771 Acc: 0.7910\n",
      "has spend time 87m 24s/n\n",
      "val Loss: 0.5493 Acc: 0.7059\n",
      "has spend time 87m 25s/n\n",
      "\n",
      "Epoch 2458/9999\n",
      "----------\n",
      "train Loss: 0.5096 Acc: 0.7213\n",
      "has spend time 87m 26s/n\n",
      "val Loss: 0.5431 Acc: 0.7255\n",
      "has spend time 87m 27s/n\n",
      "\n",
      "Epoch 2459/9999\n",
      "----------\n",
      "train Loss: 0.4974 Acc: 0.7541\n",
      "has spend time 87m 28s/n\n",
      "val Loss: 0.5525 Acc: 0.7124\n",
      "has spend time 87m 29s/n\n",
      "\n",
      "Epoch 2460/9999\n",
      "----------\n",
      "train Loss: 0.5588 Acc: 0.7090\n",
      "has spend time 87m 31s/n\n",
      "val Loss: 0.5521 Acc: 0.7190\n",
      "has spend time 87m 31s/n\n",
      "\n",
      "Epoch 2461/9999\n",
      "----------\n",
      "train Loss: 0.4888 Acc: 0.7664\n",
      "has spend time 87m 33s/n\n",
      "val Loss: 0.5558 Acc: 0.7059\n",
      "has spend time 87m 33s/n\n",
      "\n",
      "Epoch 2462/9999\n",
      "----------\n",
      "train Loss: 0.5173 Acc: 0.7500\n",
      "has spend time 87m 35s/n\n",
      "val Loss: 0.5515 Acc: 0.7124\n",
      "has spend time 87m 35s/n\n",
      "\n",
      "Epoch 2463/9999\n",
      "----------\n",
      "train Loss: 0.4904 Acc: 0.7582\n",
      "has spend time 87m 37s/n\n",
      "val Loss: 0.5597 Acc: 0.6863\n",
      "has spend time 87m 38s/n\n",
      "\n",
      "Epoch 2464/9999\n",
      "----------\n",
      "train Loss: 0.4830 Acc: 0.7377\n",
      "has spend time 87m 39s/n\n",
      "val Loss: 0.5500 Acc: 0.7124\n",
      "has spend time 87m 40s/n\n",
      "\n",
      "Epoch 2465/9999\n",
      "----------\n",
      "train Loss: 0.5167 Acc: 0.7377\n",
      "has spend time 87m 41s/n\n",
      "val Loss: 0.5523 Acc: 0.6928\n",
      "has spend time 87m 42s/n\n",
      "\n",
      "Epoch 2466/9999\n",
      "----------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train Loss: 0.5172 Acc: 0.7582\n",
      "has spend time 87m 43s/n\n",
      "val Loss: 0.5578 Acc: 0.7124\n",
      "has spend time 87m 44s/n\n",
      "\n",
      "Epoch 2467/9999\n",
      "----------\n",
      "train Loss: 0.5321 Acc: 0.7131\n",
      "has spend time 87m 45s/n\n",
      "val Loss: 0.5389 Acc: 0.7124\n",
      "has spend time 87m 46s/n\n",
      "\n",
      "Epoch 2468/9999\n",
      "----------\n",
      "train Loss: 0.4872 Acc: 0.7500\n",
      "has spend time 87m 47s/n\n",
      "val Loss: 0.5545 Acc: 0.6993\n",
      "has spend time 87m 48s/n\n",
      "\n",
      "Epoch 2469/9999\n",
      "----------\n",
      "train Loss: 0.4941 Acc: 0.7418\n",
      "has spend time 87m 49s/n\n",
      "val Loss: 0.5466 Acc: 0.6928\n",
      "has spend time 87m 50s/n\n",
      "\n",
      "Epoch 2470/9999\n",
      "----------\n",
      "train Loss: 0.4963 Acc: 0.7582\n",
      "has spend time 87m 51s/n\n",
      "val Loss: 0.5487 Acc: 0.7059\n",
      "has spend time 87m 52s/n\n",
      "\n",
      "Epoch 2471/9999\n",
      "----------\n",
      "train Loss: 0.5104 Acc: 0.7213\n",
      "has spend time 87m 53s/n\n",
      "val Loss: 0.5463 Acc: 0.7190\n",
      "has spend time 87m 54s/n\n",
      "\n",
      "Epoch 2472/9999\n",
      "----------\n",
      "train Loss: 0.5066 Acc: 0.7295\n",
      "has spend time 87m 55s/n\n",
      "val Loss: 0.5528 Acc: 0.7059\n",
      "has spend time 87m 56s/n\n",
      "\n",
      "Epoch 2473/9999\n",
      "----------\n",
      "train Loss: 0.5207 Acc: 0.7336\n",
      "has spend time 87m 57s/n\n",
      "val Loss: 0.5540 Acc: 0.6928\n",
      "has spend time 87m 58s/n\n",
      "\n",
      "Epoch 2474/9999\n",
      "----------\n",
      "train Loss: 0.5117 Acc: 0.7459\n",
      "has spend time 87m 60s/n\n",
      "val Loss: 0.5426 Acc: 0.7124\n",
      "has spend time 88m 0s/n\n",
      "\n",
      "Epoch 2475/9999\n",
      "----------\n",
      "train Loss: 0.5123 Acc: 0.7377\n",
      "has spend time 88m 2s/n\n",
      "val Loss: 0.5440 Acc: 0.7190\n",
      "has spend time 88m 2s/n\n",
      "\n",
      "Epoch 2476/9999\n",
      "----------\n",
      "train Loss: 0.5307 Acc: 0.7254\n",
      "has spend time 88m 4s/n\n",
      "val Loss: 0.5510 Acc: 0.7124\n",
      "has spend time 88m 4s/n\n",
      "\n",
      "Epoch 2477/9999\n",
      "----------\n",
      "train Loss: 0.4904 Acc: 0.7295\n",
      "has spend time 88m 6s/n\n",
      "val Loss: 0.5499 Acc: 0.7190\n",
      "has spend time 88m 7s/n\n",
      "\n",
      "Epoch 2478/9999\n",
      "----------\n",
      "train Loss: 0.5451 Acc: 0.7295\n",
      "has spend time 88m 8s/n\n",
      "val Loss: 0.5560 Acc: 0.7059\n",
      "has spend time 88m 9s/n\n",
      "\n",
      "Epoch 2479/9999\n",
      "----------\n",
      "train Loss: 0.4849 Acc: 0.7664\n",
      "has spend time 88m 10s/n\n",
      "val Loss: 0.5463 Acc: 0.7124\n",
      "has spend time 88m 11s/n\n",
      "\n",
      "Epoch 2480/9999\n",
      "----------\n",
      "train Loss: 0.5038 Acc: 0.7131\n",
      "has spend time 88m 13s/n\n",
      "val Loss: 0.5536 Acc: 0.7059\n",
      "has spend time 88m 13s/n\n",
      "\n",
      "Epoch 2481/9999\n",
      "----------\n",
      "train Loss: 0.4854 Acc: 0.7541\n",
      "has spend time 88m 15s/n\n",
      "val Loss: 0.5438 Acc: 0.7059\n",
      "has spend time 88m 15s/n\n",
      "\n",
      "Epoch 2482/9999\n",
      "----------\n",
      "train Loss: 0.4783 Acc: 0.7418\n",
      "has spend time 88m 17s/n\n",
      "val Loss: 0.5527 Acc: 0.7124\n",
      "has spend time 88m 17s/n\n",
      "\n",
      "Epoch 2483/9999\n",
      "----------\n",
      "train Loss: 0.5056 Acc: 0.7500\n",
      "has spend time 88m 19s/n\n",
      "val Loss: 0.5548 Acc: 0.7059\n",
      "has spend time 88m 20s/n\n",
      "\n",
      "Epoch 2484/9999\n",
      "----------\n",
      "train Loss: 0.4956 Acc: 0.7254\n",
      "has spend time 88m 21s/n\n",
      "val Loss: 0.5507 Acc: 0.6928\n",
      "has spend time 88m 22s/n\n",
      "\n",
      "Epoch 2485/9999\n",
      "----------\n",
      "train Loss: 0.5166 Acc: 0.7172\n",
      "has spend time 88m 23s/n\n",
      "val Loss: 0.5520 Acc: 0.7059\n",
      "has spend time 88m 24s/n\n",
      "\n",
      "Epoch 2486/9999\n",
      "----------\n",
      "train Loss: 0.5090 Acc: 0.7377\n",
      "has spend time 88m 25s/n\n",
      "val Loss: 0.5531 Acc: 0.6928\n",
      "has spend time 88m 26s/n\n",
      "\n",
      "Epoch 2487/9999\n",
      "----------\n",
      "train Loss: 0.5012 Acc: 0.7459\n",
      "has spend time 88m 27s/n\n",
      "val Loss: 0.5421 Acc: 0.7190\n",
      "has spend time 88m 28s/n\n",
      "\n",
      "Epoch 2488/9999\n",
      "----------\n",
      "train Loss: 0.4939 Acc: 0.7582\n",
      "has spend time 88m 29s/n\n",
      "val Loss: 0.5492 Acc: 0.7124\n",
      "has spend time 88m 30s/n\n",
      "\n",
      "Epoch 2489/9999\n",
      "----------\n",
      "train Loss: 0.4960 Acc: 0.7336\n",
      "has spend time 88m 32s/n\n",
      "val Loss: 0.5581 Acc: 0.7059\n",
      "has spend time 88m 32s/n\n",
      "\n",
      "Epoch 2490/9999\n",
      "----------\n",
      "train Loss: 0.4997 Acc: 0.7664\n",
      "has spend time 88m 34s/n\n",
      "val Loss: 0.5528 Acc: 0.7124\n",
      "has spend time 88m 34s/n\n",
      "\n",
      "Epoch 2491/9999\n",
      "----------\n",
      "train Loss: 0.5219 Acc: 0.7377\n",
      "has spend time 88m 36s/n\n",
      "val Loss: 0.5453 Acc: 0.7190\n",
      "has spend time 88m 36s/n\n",
      "\n",
      "Epoch 2492/9999\n",
      "----------\n",
      "train Loss: 0.5464 Acc: 0.6844\n",
      "has spend time 88m 38s/n\n",
      "val Loss: 0.5419 Acc: 0.7255\n",
      "has spend time 88m 38s/n\n",
      "\n",
      "Epoch 2493/9999\n",
      "----------\n",
      "train Loss: 0.4600 Acc: 0.7787\n",
      "has spend time 88m 40s/n\n",
      "val Loss: 0.5438 Acc: 0.7320\n",
      "has spend time 88m 40s/n\n",
      "\n",
      "Epoch 2494/9999\n",
      "----------\n",
      "train Loss: 0.5093 Acc: 0.7377\n",
      "has spend time 88m 42s/n\n",
      "val Loss: 0.5428 Acc: 0.7255\n",
      "has spend time 88m 43s/n\n",
      "\n",
      "Epoch 2495/9999\n",
      "----------\n",
      "train Loss: 0.4988 Acc: 0.7377\n",
      "has spend time 88m 44s/n\n",
      "val Loss: 0.5554 Acc: 0.6928\n",
      "has spend time 88m 45s/n\n",
      "\n",
      "Epoch 2496/9999\n",
      "----------\n",
      "train Loss: 0.5235 Acc: 0.7254\n",
      "has spend time 88m 46s/n\n",
      "val Loss: 0.5467 Acc: 0.7059\n",
      "has spend time 88m 47s/n\n",
      "\n",
      "Epoch 2497/9999\n",
      "----------\n",
      "train Loss: 0.4997 Acc: 0.7500\n",
      "has spend time 88m 48s/n\n",
      "val Loss: 0.5567 Acc: 0.6993\n",
      "has spend time 88m 49s/n\n",
      "\n",
      "Epoch 2498/9999\n",
      "----------\n",
      "train Loss: 0.5058 Acc: 0.7500\n",
      "has spend time 88m 50s/n\n",
      "val Loss: 0.5629 Acc: 0.7059\n",
      "has spend time 88m 51s/n\n",
      "\n",
      "Epoch 2499/9999\n",
      "----------\n",
      "train Loss: 0.4996 Acc: 0.7664\n",
      "has spend time 88m 52s/n\n",
      "val Loss: 0.5490 Acc: 0.7124\n",
      "has spend time 88m 53s/n\n",
      "\n",
      "Epoch 2500/9999\n",
      "----------\n",
      "train Loss: 0.5033 Acc: 0.7377\n",
      "has spend time 88m 55s/n\n",
      "val Loss: 0.5521 Acc: 0.7059\n",
      "has spend time 88m 56s/n\n",
      "\n",
      "Epoch 2501/9999\n",
      "----------\n",
      "train Loss: 0.4974 Acc: 0.7254\n",
      "has spend time 88m 57s/n\n",
      "val Loss: 0.5552 Acc: 0.7124\n",
      "has spend time 88m 58s/n\n",
      "\n",
      "Epoch 2502/9999\n",
      "----------\n",
      "train Loss: 0.5125 Acc: 0.7377\n",
      "has spend time 88m 59s/n\n",
      "val Loss: 0.5492 Acc: 0.7124\n",
      "has spend time 88m 60s/n\n",
      "\n",
      "Epoch 2503/9999\n",
      "----------\n",
      "train Loss: 0.5111 Acc: 0.7541\n",
      "has spend time 89m 1s/n\n",
      "val Loss: 0.5555 Acc: 0.7059\n",
      "has spend time 89m 2s/n\n",
      "\n",
      "Epoch 2504/9999\n",
      "----------\n",
      "train Loss: 0.5330 Acc: 0.7213\n",
      "has spend time 89m 3s/n\n",
      "val Loss: 0.5540 Acc: 0.7059\n",
      "has spend time 89m 4s/n\n",
      "\n",
      "Epoch 2505/9999\n",
      "----------\n",
      "train Loss: 0.5132 Acc: 0.7131\n",
      "has spend time 89m 5s/n\n",
      "val Loss: 0.5421 Acc: 0.7255\n",
      "has spend time 89m 6s/n\n",
      "\n",
      "Epoch 2506/9999\n",
      "----------\n",
      "train Loss: 0.5050 Acc: 0.7664\n",
      "has spend time 89m 8s/n\n",
      "val Loss: 0.5417 Acc: 0.7124\n",
      "has spend time 89m 8s/n\n",
      "\n",
      "Epoch 2507/9999\n",
      "----------\n",
      "train Loss: 0.4997 Acc: 0.7582\n",
      "has spend time 89m 10s/n\n",
      "val Loss: 0.5455 Acc: 0.7124\n",
      "has spend time 89m 10s/n\n",
      "\n",
      "Epoch 2508/9999\n",
      "----------\n",
      "train Loss: 0.4931 Acc: 0.7336\n",
      "has spend time 89m 12s/n\n",
      "val Loss: 0.5605 Acc: 0.6993\n",
      "has spend time 89m 13s/n\n",
      "\n",
      "Epoch 2509/9999\n",
      "----------\n",
      "train Loss: 0.5063 Acc: 0.7254\n",
      "has spend time 89m 14s/n\n",
      "val Loss: 0.5541 Acc: 0.6993\n",
      "has spend time 89m 15s/n\n",
      "\n",
      "Epoch 2510/9999\n",
      "----------\n",
      "train Loss: 0.5574 Acc: 0.7049\n",
      "has spend time 89m 16s/n\n",
      "val Loss: 0.5596 Acc: 0.6993\n",
      "has spend time 89m 17s/n\n",
      "\n",
      "Epoch 2511/9999\n",
      "----------\n",
      "train Loss: 0.4974 Acc: 0.7418\n",
      "has spend time 89m 18s/n\n",
      "val Loss: 0.5622 Acc: 0.6993\n",
      "has spend time 89m 19s/n\n",
      "\n",
      "Epoch 2512/9999\n",
      "----------\n",
      "train Loss: 0.5184 Acc: 0.7049\n",
      "has spend time 89m 20s/n\n",
      "val Loss: 0.5642 Acc: 0.6993\n",
      "has spend time 89m 21s/n\n",
      "\n",
      "Epoch 2513/9999\n",
      "----------\n",
      "train Loss: 0.4968 Acc: 0.7541\n",
      "has spend time 89m 22s/n\n",
      "val Loss: 0.5458 Acc: 0.7059\n",
      "has spend time 89m 23s/n\n",
      "\n",
      "Epoch 2514/9999\n",
      "----------\n",
      "train Loss: 0.4861 Acc: 0.7377\n",
      "has spend time 89m 24s/n\n",
      "val Loss: 0.5443 Acc: 0.7124\n",
      "has spend time 89m 25s/n\n",
      "\n",
      "Epoch 2515/9999\n",
      "----------\n",
      "train Loss: 0.5197 Acc: 0.7336\n",
      "has spend time 89m 26s/n\n",
      "val Loss: 0.5482 Acc: 0.6993\n",
      "has spend time 89m 27s/n\n",
      "\n",
      "Epoch 2516/9999\n",
      "----------\n",
      "train Loss: 0.5198 Acc: 0.7377\n",
      "has spend time 89m 29s/n\n",
      "val Loss: 0.5475 Acc: 0.7190\n",
      "has spend time 89m 29s/n\n",
      "\n",
      "Epoch 2517/9999\n",
      "----------\n",
      "train Loss: 0.5013 Acc: 0.7254\n",
      "has spend time 89m 31s/n\n",
      "val Loss: 0.5442 Acc: 0.7255\n",
      "has spend time 89m 31s/n\n",
      "\n",
      "Epoch 2518/9999\n",
      "----------\n",
      "train Loss: 0.5256 Acc: 0.7500\n",
      "has spend time 89m 33s/n\n",
      "val Loss: 0.5459 Acc: 0.7059\n",
      "has spend time 89m 33s/n\n",
      "\n",
      "Epoch 2519/9999\n",
      "----------\n",
      "train Loss: 0.5255 Acc: 0.7131\n",
      "has spend time 89m 35s/n\n",
      "val Loss: 0.5431 Acc: 0.7190\n",
      "has spend time 89m 35s/n\n",
      "\n",
      "Epoch 2520/9999\n",
      "----------\n",
      "train Loss: 0.4940 Acc: 0.7418\n",
      "has spend time 89m 37s/n\n",
      "val Loss: 0.5458 Acc: 0.7190\n",
      "has spend time 89m 37s/n\n",
      "\n",
      "Epoch 2521/9999\n",
      "----------\n",
      "train Loss: 0.4990 Acc: 0.7049\n",
      "has spend time 89m 39s/n\n",
      "val Loss: 0.5437 Acc: 0.7124\n",
      "has spend time 89m 39s/n\n",
      "\n",
      "Epoch 2522/9999\n",
      "----------\n",
      "train Loss: 0.5041 Acc: 0.7254\n",
      "has spend time 89m 41s/n\n",
      "val Loss: 0.5445 Acc: 0.7059\n",
      "has spend time 89m 42s/n\n",
      "\n",
      "Epoch 2523/9999\n",
      "----------\n",
      "train Loss: 0.5258 Acc: 0.7049\n",
      "has spend time 89m 43s/n\n",
      "val Loss: 0.5480 Acc: 0.7059\n",
      "has spend time 89m 44s/n\n",
      "\n",
      "Epoch 2524/9999\n",
      "----------\n",
      "train Loss: 0.4740 Acc: 0.7336\n",
      "has spend time 89m 46s/n\n",
      "val Loss: 0.5461 Acc: 0.7190\n",
      "has spend time 89m 46s/n\n",
      "\n",
      "Epoch 2525/9999\n",
      "----------\n",
      "train Loss: 0.5075 Acc: 0.7254\n",
      "has spend time 89m 48s/n\n",
      "val Loss: 0.5434 Acc: 0.7190\n",
      "has spend time 89m 48s/n\n",
      "\n",
      "Epoch 2526/9999\n",
      "----------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train Loss: 0.4965 Acc: 0.7500\n",
      "has spend time 89m 50s/n\n",
      "val Loss: 0.5633 Acc: 0.6993\n",
      "has spend time 89m 51s/n\n",
      "\n",
      "Epoch 2527/9999\n",
      "----------\n",
      "train Loss: 0.4884 Acc: 0.7582\n",
      "has spend time 89m 52s/n\n",
      "val Loss: 0.5666 Acc: 0.6928\n",
      "has spend time 89m 53s/n\n",
      "\n",
      "Epoch 2528/9999\n",
      "----------\n",
      "train Loss: 0.4933 Acc: 0.7254\n",
      "has spend time 89m 54s/n\n",
      "val Loss: 0.5675 Acc: 0.6993\n",
      "has spend time 89m 55s/n\n",
      "\n",
      "Epoch 2529/9999\n",
      "----------\n",
      "train Loss: 0.5215 Acc: 0.7172\n",
      "has spend time 89m 56s/n\n",
      "val Loss: 0.5683 Acc: 0.6928\n",
      "has spend time 89m 57s/n\n",
      "\n",
      "Epoch 2530/9999\n",
      "----------\n",
      "train Loss: 0.5130 Acc: 0.7459\n",
      "has spend time 89m 58s/n\n",
      "val Loss: 0.5605 Acc: 0.7059\n",
      "has spend time 89m 59s/n\n",
      "\n",
      "Epoch 2531/9999\n",
      "----------\n",
      "train Loss: 0.5022 Acc: 0.7500\n",
      "has spend time 90m 0s/n\n",
      "val Loss: 0.5596 Acc: 0.7124\n",
      "has spend time 90m 1s/n\n",
      "\n",
      "Epoch 2532/9999\n",
      "----------\n",
      "train Loss: 0.5377 Acc: 0.7418\n",
      "has spend time 90m 2s/n\n",
      "val Loss: 0.5685 Acc: 0.6993\n",
      "has spend time 90m 3s/n\n",
      "\n",
      "Epoch 2533/9999\n",
      "----------\n",
      "train Loss: 0.4828 Acc: 0.7582\n",
      "has spend time 90m 4s/n\n",
      "val Loss: 0.5584 Acc: 0.6928\n",
      "has spend time 90m 5s/n\n",
      "\n",
      "Epoch 2534/9999\n",
      "----------\n",
      "train Loss: 0.5381 Acc: 0.7213\n",
      "has spend time 90m 6s/n\n",
      "val Loss: 0.5451 Acc: 0.6993\n",
      "has spend time 90m 7s/n\n",
      "\n",
      "Epoch 2535/9999\n",
      "----------\n",
      "train Loss: 0.4990 Acc: 0.7910\n",
      "has spend time 90m 8s/n\n",
      "val Loss: 0.5412 Acc: 0.7255\n",
      "has spend time 90m 9s/n\n",
      "\n",
      "Epoch 2536/9999\n",
      "----------\n",
      "train Loss: 0.5141 Acc: 0.7418\n",
      "has spend time 90m 11s/n\n",
      "val Loss: 0.5528 Acc: 0.7059\n",
      "has spend time 90m 12s/n\n",
      "\n",
      "Epoch 2537/9999\n",
      "----------\n",
      "train Loss: 0.5374 Acc: 0.7008\n",
      "has spend time 90m 13s/n\n",
      "val Loss: 0.5539 Acc: 0.6928\n",
      "has spend time 90m 14s/n\n",
      "\n",
      "Epoch 2538/9999\n",
      "----------\n",
      "train Loss: 0.5479 Acc: 0.7295\n",
      "has spend time 90m 15s/n\n",
      "val Loss: 0.5499 Acc: 0.7124\n",
      "has spend time 90m 16s/n\n",
      "\n",
      "Epoch 2539/9999\n",
      "----------\n",
      "train Loss: 0.5177 Acc: 0.7049\n",
      "has spend time 90m 17s/n\n",
      "val Loss: 0.5453 Acc: 0.7124\n",
      "has spend time 90m 18s/n\n",
      "\n",
      "Epoch 2540/9999\n",
      "----------\n",
      "train Loss: 0.4791 Acc: 0.7295\n",
      "has spend time 90m 19s/n\n",
      "val Loss: 0.5480 Acc: 0.7059\n",
      "has spend time 90m 20s/n\n",
      "\n",
      "Epoch 2541/9999\n",
      "----------\n",
      "train Loss: 0.4922 Acc: 0.7418\n",
      "has spend time 90m 21s/n\n",
      "val Loss: 0.5551 Acc: 0.6993\n",
      "has spend time 90m 22s/n\n",
      "\n",
      "Epoch 2542/9999\n",
      "----------\n",
      "train Loss: 0.5203 Acc: 0.7172\n",
      "has spend time 90m 23s/n\n",
      "val Loss: 0.5427 Acc: 0.7255\n",
      "has spend time 90m 24s/n\n",
      "\n",
      "Epoch 2543/9999\n",
      "----------\n",
      "train Loss: 0.5026 Acc: 0.7418\n",
      "has spend time 90m 25s/n\n",
      "val Loss: 0.5342 Acc: 0.7190\n",
      "has spend time 90m 26s/n\n",
      "\n",
      "Epoch 2544/9999\n",
      "----------\n",
      "train Loss: 0.5247 Acc: 0.7131\n",
      "has spend time 90m 28s/n\n",
      "val Loss: 0.5474 Acc: 0.7124\n",
      "has spend time 90m 28s/n\n",
      "\n",
      "Epoch 2545/9999\n",
      "----------\n",
      "train Loss: 0.4870 Acc: 0.7295\n",
      "has spend time 90m 30s/n\n",
      "val Loss: 0.5517 Acc: 0.7059\n",
      "has spend time 90m 30s/n\n",
      "\n",
      "Epoch 2546/9999\n",
      "----------\n",
      "train Loss: 0.4877 Acc: 0.7418\n",
      "has spend time 90m 32s/n\n",
      "val Loss: 0.5636 Acc: 0.6993\n",
      "has spend time 90m 33s/n\n",
      "\n",
      "Epoch 2547/9999\n",
      "----------\n",
      "train Loss: 0.5141 Acc: 0.7459\n",
      "has spend time 90m 34s/n\n",
      "val Loss: 0.5562 Acc: 0.7059\n",
      "has spend time 90m 35s/n\n",
      "\n",
      "Epoch 2548/9999\n",
      "----------\n",
      "train Loss: 0.5135 Acc: 0.7418\n",
      "has spend time 90m 36s/n\n",
      "val Loss: 0.5570 Acc: 0.7059\n",
      "has spend time 90m 37s/n\n",
      "\n",
      "Epoch 2549/9999\n",
      "----------\n",
      "train Loss: 0.5150 Acc: 0.7377\n",
      "has spend time 90m 38s/n\n",
      "val Loss: 0.5468 Acc: 0.7190\n",
      "has spend time 90m 39s/n\n",
      "\n",
      "Epoch 2550/9999\n",
      "----------\n",
      "train Loss: 0.5129 Acc: 0.7172\n",
      "has spend time 90m 40s/n\n",
      "val Loss: 0.5540 Acc: 0.7059\n",
      "has spend time 90m 41s/n\n",
      "\n",
      "Epoch 2551/9999\n",
      "----------\n",
      "train Loss: 0.4994 Acc: 0.7459\n",
      "has spend time 90m 42s/n\n",
      "val Loss: 0.5434 Acc: 0.7124\n",
      "has spend time 90m 43s/n\n",
      "\n",
      "Epoch 2552/9999\n",
      "----------\n",
      "train Loss: 0.5238 Acc: 0.7049\n",
      "has spend time 90m 45s/n\n",
      "val Loss: 0.5527 Acc: 0.7124\n",
      "has spend time 90m 45s/n\n",
      "\n",
      "Epoch 2553/9999\n",
      "----------\n",
      "train Loss: 0.5336 Acc: 0.6844\n",
      "has spend time 90m 47s/n\n",
      "val Loss: 0.5565 Acc: 0.6993\n",
      "has spend time 90m 48s/n\n",
      "\n",
      "Epoch 2554/9999\n",
      "----------\n",
      "train Loss: 0.5123 Acc: 0.7295\n",
      "has spend time 90m 49s/n\n",
      "val Loss: 0.5636 Acc: 0.6928\n",
      "has spend time 90m 50s/n\n",
      "\n",
      "Epoch 2555/9999\n",
      "----------\n",
      "train Loss: 0.5032 Acc: 0.7295\n",
      "has spend time 90m 51s/n\n",
      "val Loss: 0.5498 Acc: 0.7124\n",
      "has spend time 90m 52s/n\n",
      "\n",
      "Epoch 2556/9999\n",
      "----------\n",
      "train Loss: 0.5020 Acc: 0.7377\n",
      "has spend time 90m 53s/n\n",
      "val Loss: 0.5517 Acc: 0.7190\n",
      "has spend time 90m 54s/n\n",
      "\n",
      "Epoch 2557/9999\n",
      "----------\n",
      "train Loss: 0.5215 Acc: 0.7541\n",
      "has spend time 90m 55s/n\n",
      "val Loss: 0.5503 Acc: 0.7059\n",
      "has spend time 90m 56s/n\n",
      "\n",
      "Epoch 2558/9999\n",
      "----------\n",
      "train Loss: 0.5120 Acc: 0.7336\n",
      "has spend time 90m 57s/n\n",
      "val Loss: 0.5635 Acc: 0.6993\n",
      "has spend time 90m 58s/n\n",
      "\n",
      "Epoch 2559/9999\n",
      "----------\n",
      "train Loss: 0.5174 Acc: 0.7336\n",
      "has spend time 90m 59s/n\n",
      "val Loss: 0.5497 Acc: 0.7190\n",
      "has spend time 90m 60s/n\n",
      "\n",
      "Epoch 2560/9999\n",
      "----------\n",
      "train Loss: 0.4888 Acc: 0.7746\n",
      "has spend time 91m 1s/n\n",
      "val Loss: 0.5486 Acc: 0.7059\n",
      "has spend time 91m 2s/n\n",
      "\n",
      "Epoch 2561/9999\n",
      "----------\n",
      "train Loss: 0.4756 Acc: 0.7869\n",
      "has spend time 91m 4s/n\n",
      "val Loss: 0.5457 Acc: 0.6928\n",
      "has spend time 91m 5s/n\n",
      "\n",
      "Epoch 2562/9999\n",
      "----------\n",
      "train Loss: 0.5182 Acc: 0.7623\n",
      "has spend time 91m 6s/n\n",
      "val Loss: 0.5447 Acc: 0.6993\n",
      "has spend time 91m 7s/n\n",
      "\n",
      "Epoch 2563/9999\n",
      "----------\n",
      "train Loss: 0.5189 Acc: 0.7131\n",
      "has spend time 91m 8s/n\n",
      "val Loss: 0.5535 Acc: 0.7190\n",
      "has spend time 91m 9s/n\n",
      "\n",
      "Epoch 2564/9999\n",
      "----------\n",
      "train Loss: 0.4886 Acc: 0.7582\n",
      "has spend time 91m 10s/n\n",
      "val Loss: 0.5593 Acc: 0.7059\n",
      "has spend time 91m 11s/n\n",
      "\n",
      "Epoch 2565/9999\n",
      "----------\n",
      "train Loss: 0.5081 Acc: 0.7336\n",
      "has spend time 91m 12s/n\n",
      "val Loss: 0.5485 Acc: 0.7059\n",
      "has spend time 91m 13s/n\n",
      "\n",
      "Epoch 2566/9999\n",
      "----------\n",
      "train Loss: 0.5090 Acc: 0.7418\n",
      "has spend time 91m 14s/n\n",
      "val Loss: 0.5592 Acc: 0.6993\n",
      "has spend time 91m 15s/n\n",
      "\n",
      "Epoch 2567/9999\n",
      "----------\n",
      "train Loss: 0.4771 Acc: 0.7418\n",
      "has spend time 91m 17s/n\n",
      "val Loss: 0.5472 Acc: 0.7124\n",
      "has spend time 91m 18s/n\n",
      "\n",
      "Epoch 2568/9999\n",
      "----------\n",
      "train Loss: 0.5238 Acc: 0.7541\n",
      "has spend time 91m 19s/n\n",
      "val Loss: 0.5557 Acc: 0.7059\n",
      "has spend time 91m 20s/n\n",
      "\n",
      "Epoch 2569/9999\n",
      "----------\n",
      "train Loss: 0.4921 Acc: 0.7787\n",
      "has spend time 91m 21s/n\n",
      "val Loss: 0.5543 Acc: 0.7059\n",
      "has spend time 91m 22s/n\n",
      "\n",
      "Epoch 2570/9999\n",
      "----------\n",
      "train Loss: 0.5298 Acc: 0.7254\n",
      "has spend time 91m 23s/n\n",
      "val Loss: 0.5506 Acc: 0.7059\n",
      "has spend time 91m 24s/n\n",
      "\n",
      "Epoch 2571/9999\n",
      "----------\n",
      "train Loss: 0.5006 Acc: 0.7623\n",
      "has spend time 91m 25s/n\n",
      "val Loss: 0.5429 Acc: 0.7190\n",
      "has spend time 91m 26s/n\n",
      "\n",
      "Epoch 2572/9999\n",
      "----------\n",
      "train Loss: 0.5034 Acc: 0.7254\n",
      "has spend time 91m 27s/n\n",
      "val Loss: 0.5562 Acc: 0.6993\n",
      "has spend time 91m 28s/n\n",
      "\n",
      "Epoch 2573/9999\n",
      "----------\n",
      "train Loss: 0.5039 Acc: 0.7459\n",
      "has spend time 91m 29s/n\n",
      "val Loss: 0.5475 Acc: 0.7124\n",
      "has spend time 91m 30s/n\n",
      "\n",
      "Epoch 2574/9999\n",
      "----------\n",
      "train Loss: 0.5239 Acc: 0.7049\n",
      "has spend time 91m 32s/n\n",
      "val Loss: 0.5449 Acc: 0.7190\n",
      "has spend time 91m 32s/n\n",
      "\n",
      "Epoch 2575/9999\n",
      "----------\n",
      "train Loss: 0.5156 Acc: 0.7377\n",
      "has spend time 91m 34s/n\n",
      "val Loss: 0.5471 Acc: 0.7190\n",
      "has spend time 91m 34s/n\n",
      "\n",
      "Epoch 2576/9999\n",
      "----------\n",
      "train Loss: 0.5191 Acc: 0.7008\n",
      "has spend time 91m 36s/n\n",
      "val Loss: 0.5434 Acc: 0.7059\n",
      "has spend time 91m 36s/n\n",
      "\n",
      "Epoch 2577/9999\n",
      "----------\n",
      "train Loss: 0.4878 Acc: 0.7623\n",
      "has spend time 91m 38s/n\n",
      "val Loss: 0.5415 Acc: 0.7124\n",
      "has spend time 91m 39s/n\n",
      "\n",
      "Epoch 2578/9999\n",
      "----------\n",
      "train Loss: 0.4887 Acc: 0.7377\n",
      "has spend time 91m 40s/n\n",
      "val Loss: 0.5480 Acc: 0.7255\n",
      "has spend time 91m 41s/n\n",
      "\n",
      "Epoch 2579/9999\n",
      "----------\n",
      "train Loss: 0.5260 Acc: 0.7377\n",
      "has spend time 91m 42s/n\n",
      "val Loss: 0.5667 Acc: 0.6863\n",
      "has spend time 91m 43s/n\n",
      "\n",
      "Epoch 2580/9999\n",
      "----------\n",
      "train Loss: 0.4977 Acc: 0.7336\n",
      "has spend time 91m 44s/n\n",
      "val Loss: 0.5640 Acc: 0.6993\n",
      "has spend time 91m 45s/n\n",
      "\n",
      "Epoch 2581/9999\n",
      "----------\n",
      "train Loss: 0.5278 Acc: 0.7336\n",
      "has spend time 91m 46s/n\n",
      "val Loss: 0.5507 Acc: 0.7190\n",
      "has spend time 91m 47s/n\n",
      "\n",
      "Epoch 2582/9999\n",
      "----------\n",
      "train Loss: 0.5136 Acc: 0.7049\n",
      "has spend time 91m 48s/n\n",
      "val Loss: 0.5539 Acc: 0.7059\n",
      "has spend time 91m 49s/n\n",
      "\n",
      "Epoch 2583/9999\n",
      "----------\n",
      "train Loss: 0.5013 Acc: 0.7623\n",
      "has spend time 91m 50s/n\n",
      "val Loss: 0.5475 Acc: 0.7190\n",
      "has spend time 91m 51s/n\n",
      "\n",
      "Epoch 2584/9999\n",
      "----------\n",
      "train Loss: 0.5147 Acc: 0.7336\n",
      "has spend time 91m 52s/n\n",
      "val Loss: 0.5498 Acc: 0.7124\n",
      "has spend time 91m 53s/n\n",
      "\n",
      "Epoch 2585/9999\n",
      "----------\n",
      "train Loss: 0.5152 Acc: 0.7295\n",
      "has spend time 91m 55s/n\n",
      "val Loss: 0.5436 Acc: 0.7059\n",
      "has spend time 91m 55s/n\n",
      "\n",
      "Epoch 2586/9999\n",
      "----------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train Loss: 0.5284 Acc: 0.7213\n",
      "has spend time 91m 57s/n\n",
      "val Loss: 0.5635 Acc: 0.6928\n",
      "has spend time 91m 57s/n\n",
      "\n",
      "Epoch 2587/9999\n",
      "----------\n",
      "train Loss: 0.5444 Acc: 0.7295\n",
      "has spend time 91m 59s/n\n",
      "val Loss: 0.5530 Acc: 0.7059\n",
      "has spend time 91m 60s/n\n",
      "\n",
      "Epoch 2588/9999\n",
      "----------\n",
      "train Loss: 0.5131 Acc: 0.7131\n",
      "has spend time 92m 1s/n\n",
      "val Loss: 0.5457 Acc: 0.7255\n",
      "has spend time 92m 2s/n\n",
      "\n",
      "Epoch 2589/9999\n",
      "----------\n",
      "train Loss: 0.5071 Acc: 0.7254\n",
      "has spend time 92m 3s/n\n",
      "val Loss: 0.5424 Acc: 0.7190\n",
      "has spend time 92m 4s/n\n",
      "\n",
      "Epoch 2590/9999\n",
      "----------\n",
      "train Loss: 0.5084 Acc: 0.7336\n",
      "has spend time 92m 6s/n\n",
      "val Loss: 0.5560 Acc: 0.7059\n",
      "has spend time 92m 6s/n\n",
      "\n",
      "Epoch 2591/9999\n",
      "----------\n",
      "train Loss: 0.5306 Acc: 0.7090\n",
      "has spend time 92m 8s/n\n",
      "val Loss: 0.5437 Acc: 0.7190\n",
      "has spend time 92m 8s/n\n",
      "\n",
      "Epoch 2592/9999\n",
      "----------\n",
      "train Loss: 0.5262 Acc: 0.7008\n",
      "has spend time 92m 10s/n\n",
      "val Loss: 0.5476 Acc: 0.7124\n",
      "has spend time 92m 10s/n\n",
      "\n",
      "Epoch 2593/9999\n",
      "----------\n",
      "train Loss: 0.5245 Acc: 0.7336\n",
      "has spend time 92m 12s/n\n",
      "val Loss: 0.5492 Acc: 0.7059\n",
      "has spend time 92m 12s/n\n",
      "\n",
      "Epoch 2594/9999\n",
      "----------\n",
      "train Loss: 0.5140 Acc: 0.7213\n",
      "has spend time 92m 14s/n\n",
      "val Loss: 0.5430 Acc: 0.7124\n",
      "has spend time 92m 15s/n\n",
      "\n",
      "Epoch 2595/9999\n",
      "----------\n",
      "train Loss: 0.5085 Acc: 0.7295\n",
      "has spend time 92m 16s/n\n",
      "val Loss: 0.5555 Acc: 0.7124\n",
      "has spend time 92m 17s/n\n",
      "\n",
      "Epoch 2596/9999\n",
      "----------\n",
      "train Loss: 0.4919 Acc: 0.7623\n",
      "has spend time 92m 18s/n\n",
      "val Loss: 0.5533 Acc: 0.7190\n",
      "has spend time 92m 19s/n\n",
      "\n",
      "Epoch 2597/9999\n",
      "----------\n",
      "train Loss: 0.5240 Acc: 0.7295\n",
      "has spend time 92m 20s/n\n",
      "val Loss: 0.5491 Acc: 0.7124\n",
      "has spend time 92m 21s/n\n",
      "\n",
      "Epoch 2598/9999\n",
      "----------\n",
      "train Loss: 0.5108 Acc: 0.7295\n",
      "has spend time 92m 22s/n\n",
      "val Loss: 0.5456 Acc: 0.7124\n",
      "has spend time 92m 23s/n\n",
      "\n",
      "Epoch 2599/9999\n",
      "----------\n",
      "train Loss: 0.5125 Acc: 0.7254\n",
      "has spend time 92m 24s/n\n",
      "val Loss: 0.5470 Acc: 0.7190\n",
      "has spend time 92m 25s/n\n",
      "\n",
      "Epoch 2600/9999\n",
      "----------\n",
      "train Loss: 0.4966 Acc: 0.7213\n",
      "has spend time 92m 26s/n\n",
      "val Loss: 0.5531 Acc: 0.7059\n",
      "has spend time 92m 27s/n\n",
      "\n",
      "Epoch 2601/9999\n",
      "----------\n",
      "train Loss: 0.4995 Acc: 0.7500\n",
      "has spend time 92m 28s/n\n",
      "val Loss: 0.5462 Acc: 0.7059\n",
      "has spend time 92m 29s/n\n",
      "\n",
      "Epoch 2602/9999\n",
      "----------\n",
      "train Loss: 0.4865 Acc: 0.7705\n",
      "has spend time 92m 30s/n\n",
      "val Loss: 0.5503 Acc: 0.7190\n",
      "has spend time 92m 31s/n\n",
      "\n",
      "Epoch 2603/9999\n",
      "----------\n",
      "train Loss: 0.5183 Acc: 0.7008\n",
      "has spend time 92m 33s/n\n",
      "val Loss: 0.5502 Acc: 0.7124\n",
      "has spend time 92m 34s/n\n",
      "\n",
      "Epoch 2604/9999\n",
      "----------\n",
      "train Loss: 0.5014 Acc: 0.7090\n",
      "has spend time 92m 35s/n\n",
      "val Loss: 0.5487 Acc: 0.7190\n",
      "has spend time 92m 36s/n\n",
      "\n",
      "Epoch 2605/9999\n",
      "----------\n",
      "train Loss: 0.5052 Acc: 0.7541\n",
      "has spend time 92m 37s/n\n",
      "val Loss: 0.5539 Acc: 0.7190\n",
      "has spend time 92m 38s/n\n",
      "\n",
      "Epoch 2606/9999\n",
      "----------\n",
      "train Loss: 0.5080 Acc: 0.7500\n",
      "has spend time 92m 39s/n\n",
      "val Loss: 0.5578 Acc: 0.7059\n",
      "has spend time 92m 40s/n\n",
      "\n",
      "Epoch 2607/9999\n",
      "----------\n",
      "train Loss: 0.5039 Acc: 0.7377\n",
      "has spend time 92m 41s/n\n",
      "val Loss: 0.5458 Acc: 0.7190\n",
      "has spend time 92m 42s/n\n",
      "\n",
      "Epoch 2608/9999\n",
      "----------\n",
      "train Loss: 0.5149 Acc: 0.7213\n",
      "has spend time 92m 43s/n\n",
      "val Loss: 0.5501 Acc: 0.7255\n",
      "has spend time 92m 44s/n\n",
      "\n",
      "Epoch 2609/9999\n",
      "----------\n",
      "train Loss: 0.5008 Acc: 0.7254\n",
      "has spend time 92m 46s/n\n",
      "val Loss: 0.5560 Acc: 0.7059\n",
      "has spend time 92m 46s/n\n",
      "\n",
      "Epoch 2610/9999\n",
      "----------\n",
      "train Loss: 0.5178 Acc: 0.7295\n",
      "has spend time 92m 48s/n\n",
      "val Loss: 0.5461 Acc: 0.7190\n",
      "has spend time 92m 48s/n\n",
      "\n",
      "Epoch 2611/9999\n",
      "----------\n",
      "train Loss: 0.5273 Acc: 0.7090\n",
      "has spend time 92m 50s/n\n",
      "val Loss: 0.5458 Acc: 0.7124\n",
      "has spend time 92m 50s/n\n",
      "\n",
      "Epoch 2612/9999\n",
      "----------\n",
      "train Loss: 0.5481 Acc: 0.6967\n",
      "has spend time 92m 52s/n\n",
      "val Loss: 0.5504 Acc: 0.7124\n",
      "has spend time 92m 52s/n\n",
      "\n",
      "Epoch 2613/9999\n",
      "----------\n",
      "train Loss: 0.4965 Acc: 0.7418\n",
      "has spend time 92m 54s/n\n",
      "val Loss: 0.5467 Acc: 0.7059\n",
      "has spend time 92m 55s/n\n",
      "\n",
      "Epoch 2614/9999\n",
      "----------\n",
      "train Loss: 0.5178 Acc: 0.7541\n",
      "has spend time 92m 56s/n\n",
      "val Loss: 0.5526 Acc: 0.7059\n",
      "has spend time 92m 57s/n\n",
      "\n",
      "Epoch 2615/9999\n",
      "----------\n",
      "train Loss: 0.5253 Acc: 0.7377\n",
      "has spend time 92m 58s/n\n",
      "val Loss: 0.5579 Acc: 0.7059\n",
      "has spend time 92m 59s/n\n",
      "\n",
      "Epoch 2616/9999\n",
      "----------\n",
      "train Loss: 0.5403 Acc: 0.7459\n",
      "has spend time 93m 0s/n\n",
      "val Loss: 0.5518 Acc: 0.7124\n",
      "has spend time 93m 1s/n\n",
      "\n",
      "Epoch 2617/9999\n",
      "----------\n",
      "train Loss: 0.5363 Acc: 0.7090\n",
      "has spend time 93m 2s/n\n",
      "val Loss: 0.5650 Acc: 0.6928\n",
      "has spend time 93m 3s/n\n",
      "\n",
      "Epoch 2618/9999\n",
      "----------\n",
      "train Loss: 0.5146 Acc: 0.7541\n",
      "has spend time 93m 4s/n\n",
      "val Loss: 0.5547 Acc: 0.7059\n",
      "has spend time 93m 5s/n\n",
      "\n",
      "Epoch 2619/9999\n",
      "----------\n",
      "train Loss: 0.5240 Acc: 0.7213\n",
      "has spend time 93m 6s/n\n",
      "val Loss: 0.5489 Acc: 0.7190\n",
      "has spend time 93m 7s/n\n",
      "\n",
      "Epoch 2620/9999\n",
      "----------\n",
      "train Loss: 0.5070 Acc: 0.7418\n",
      "has spend time 93m 9s/n\n",
      "val Loss: 0.5413 Acc: 0.7124\n",
      "has spend time 93m 10s/n\n",
      "\n",
      "Epoch 2621/9999\n",
      "----------\n",
      "train Loss: 0.4881 Acc: 0.7500\n",
      "has spend time 93m 11s/n\n",
      "val Loss: 0.5459 Acc: 0.7190\n",
      "has spend time 93m 12s/n\n",
      "\n",
      "Epoch 2622/9999\n",
      "----------\n",
      "train Loss: 0.5012 Acc: 0.7500\n",
      "has spend time 93m 13s/n\n",
      "val Loss: 0.5607 Acc: 0.6993\n",
      "has spend time 93m 14s/n\n",
      "\n",
      "Epoch 2623/9999\n",
      "----------\n",
      "train Loss: 0.5202 Acc: 0.7254\n",
      "has spend time 93m 15s/n\n",
      "val Loss: 0.5548 Acc: 0.7059\n",
      "has spend time 93m 16s/n\n",
      "\n",
      "Epoch 2624/9999\n",
      "----------\n",
      "train Loss: 0.5261 Acc: 0.7336\n",
      "has spend time 93m 17s/n\n",
      "val Loss: 0.5494 Acc: 0.7190\n",
      "has spend time 93m 18s/n\n",
      "\n",
      "Epoch 2625/9999\n",
      "----------\n",
      "train Loss: 0.5140 Acc: 0.7254\n",
      "has spend time 93m 19s/n\n",
      "val Loss: 0.5465 Acc: 0.7124\n",
      "has spend time 93m 20s/n\n",
      "\n",
      "Epoch 2626/9999\n",
      "----------\n",
      "train Loss: 0.4962 Acc: 0.7541\n",
      "has spend time 93m 22s/n\n",
      "val Loss: 0.5482 Acc: 0.7190\n",
      "has spend time 93m 23s/n\n",
      "\n",
      "Epoch 2627/9999\n",
      "----------\n",
      "train Loss: 0.5498 Acc: 0.7172\n",
      "has spend time 93m 24s/n\n",
      "val Loss: 0.5474 Acc: 0.6993\n",
      "has spend time 93m 25s/n\n",
      "\n",
      "Epoch 2628/9999\n",
      "----------\n",
      "train Loss: 0.4923 Acc: 0.7377\n",
      "has spend time 93m 26s/n\n",
      "val Loss: 0.5454 Acc: 0.7190\n",
      "has spend time 93m 27s/n\n",
      "\n",
      "Epoch 2629/9999\n",
      "----------\n",
      "train Loss: 0.4973 Acc: 0.7418\n",
      "has spend time 93m 28s/n\n",
      "val Loss: 0.5510 Acc: 0.6993\n",
      "has spend time 93m 29s/n\n",
      "\n",
      "Epoch 2630/9999\n",
      "----------\n",
      "train Loss: 0.5050 Acc: 0.7582\n",
      "has spend time 93m 31s/n\n",
      "val Loss: 0.5592 Acc: 0.6993\n",
      "has spend time 93m 31s/n\n",
      "\n",
      "Epoch 2631/9999\n",
      "----------\n",
      "train Loss: 0.5376 Acc: 0.7295\n",
      "has spend time 93m 33s/n\n",
      "val Loss: 0.5543 Acc: 0.7124\n",
      "has spend time 93m 33s/n\n",
      "\n",
      "Epoch 2632/9999\n",
      "----------\n",
      "train Loss: 0.5329 Acc: 0.7131\n",
      "has spend time 93m 35s/n\n",
      "val Loss: 0.5663 Acc: 0.7059\n",
      "has spend time 93m 35s/n\n",
      "\n",
      "Epoch 2633/9999\n",
      "----------\n",
      "train Loss: 0.5016 Acc: 0.7254\n",
      "has spend time 93m 37s/n\n",
      "val Loss: 0.5521 Acc: 0.6928\n",
      "has spend time 93m 37s/n\n",
      "\n",
      "Epoch 2634/9999\n",
      "----------\n",
      "train Loss: 0.4915 Acc: 0.7459\n",
      "has spend time 93m 39s/n\n",
      "val Loss: 0.5601 Acc: 0.6993\n",
      "has spend time 93m 39s/n\n",
      "\n",
      "Epoch 2635/9999\n",
      "----------\n",
      "train Loss: 0.5001 Acc: 0.7377\n",
      "has spend time 93m 41s/n\n",
      "val Loss: 0.5613 Acc: 0.6797\n",
      "has spend time 93m 41s/n\n",
      "\n",
      "Epoch 2636/9999\n",
      "----------\n",
      "train Loss: 0.4906 Acc: 0.7459\n",
      "has spend time 93m 43s/n\n",
      "val Loss: 0.5452 Acc: 0.7320\n",
      "has spend time 93m 44s/n\n",
      "\n",
      "Epoch 2637/9999\n",
      "----------\n",
      "train Loss: 0.5086 Acc: 0.7336\n",
      "has spend time 93m 45s/n\n",
      "val Loss: 0.5367 Acc: 0.7255\n",
      "has spend time 93m 46s/n\n",
      "\n",
      "Epoch 2638/9999\n",
      "----------\n",
      "train Loss: 0.5256 Acc: 0.7254\n",
      "has spend time 93m 47s/n\n",
      "val Loss: 0.5454 Acc: 0.7124\n",
      "has spend time 93m 48s/n\n",
      "\n",
      "Epoch 2639/9999\n",
      "----------\n",
      "train Loss: 0.5079 Acc: 0.7418\n",
      "has spend time 93m 49s/n\n",
      "val Loss: 0.5444 Acc: 0.7059\n",
      "has spend time 93m 50s/n\n",
      "\n",
      "Epoch 2640/9999\n",
      "----------\n",
      "train Loss: 0.4959 Acc: 0.7377\n",
      "has spend time 93m 51s/n\n",
      "val Loss: 0.5473 Acc: 0.6993\n",
      "has spend time 93m 52s/n\n",
      "\n",
      "Epoch 2641/9999\n",
      "----------\n",
      "train Loss: 0.4967 Acc: 0.7541\n",
      "has spend time 93m 53s/n\n",
      "val Loss: 0.5545 Acc: 0.6993\n",
      "has spend time 93m 54s/n\n",
      "\n",
      "Epoch 2642/9999\n",
      "----------\n",
      "train Loss: 0.5279 Acc: 0.7295\n",
      "has spend time 93m 55s/n\n",
      "val Loss: 0.5440 Acc: 0.7124\n",
      "has spend time 93m 56s/n\n",
      "\n",
      "Epoch 2643/9999\n",
      "----------\n",
      "train Loss: 0.5110 Acc: 0.7541\n",
      "has spend time 93m 58s/n\n",
      "val Loss: 0.5424 Acc: 0.7124\n",
      "has spend time 93m 58s/n\n",
      "\n",
      "Epoch 2644/9999\n",
      "----------\n",
      "train Loss: 0.5344 Acc: 0.7172\n",
      "has spend time 93m 60s/n\n",
      "val Loss: 0.5523 Acc: 0.7124\n",
      "has spend time 94m 0s/n\n",
      "\n",
      "Epoch 2645/9999\n",
      "----------\n",
      "train Loss: 0.5019 Acc: 0.7295\n",
      "has spend time 94m 2s/n\n",
      "val Loss: 0.5662 Acc: 0.6928\n",
      "has spend time 94m 3s/n\n",
      "\n",
      "Epoch 2646/9999\n",
      "----------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train Loss: 0.5026 Acc: 0.7131\n",
      "has spend time 94m 4s/n\n",
      "val Loss: 0.5598 Acc: 0.6993\n",
      "has spend time 94m 5s/n\n",
      "\n",
      "Epoch 2647/9999\n",
      "----------\n",
      "train Loss: 0.5260 Acc: 0.6967\n",
      "has spend time 94m 6s/n\n",
      "val Loss: 0.5532 Acc: 0.7124\n",
      "has spend time 94m 7s/n\n",
      "\n",
      "Epoch 2648/9999\n",
      "----------\n",
      "train Loss: 0.5318 Acc: 0.7131\n",
      "has spend time 94m 8s/n\n",
      "val Loss: 0.5579 Acc: 0.6993\n",
      "has spend time 94m 9s/n\n",
      "\n",
      "Epoch 2649/9999\n",
      "----------\n",
      "train Loss: 0.5136 Acc: 0.7418\n",
      "has spend time 94m 10s/n\n",
      "val Loss: 0.5418 Acc: 0.7190\n",
      "has spend time 94m 11s/n\n",
      "\n",
      "Epoch 2650/9999\n",
      "----------\n",
      "train Loss: 0.4905 Acc: 0.7254\n",
      "has spend time 94m 13s/n\n",
      "val Loss: 0.5418 Acc: 0.7059\n",
      "has spend time 94m 13s/n\n",
      "\n",
      "Epoch 2651/9999\n",
      "----------\n",
      "train Loss: 0.4790 Acc: 0.7336\n",
      "has spend time 94m 15s/n\n",
      "val Loss: 0.5452 Acc: 0.7059\n",
      "has spend time 94m 15s/n\n",
      "\n",
      "Epoch 2652/9999\n",
      "----------\n",
      "train Loss: 0.5218 Acc: 0.7213\n",
      "has spend time 94m 17s/n\n",
      "val Loss: 0.5428 Acc: 0.7190\n",
      "has spend time 94m 17s/n\n",
      "\n",
      "Epoch 2653/9999\n",
      "----------\n",
      "train Loss: 0.5138 Acc: 0.7295\n",
      "has spend time 94m 19s/n\n",
      "val Loss: 0.5362 Acc: 0.7255\n",
      "has spend time 94m 19s/n\n",
      "\n",
      "Epoch 2654/9999\n",
      "----------\n",
      "train Loss: 0.5030 Acc: 0.7336\n",
      "has spend time 94m 21s/n\n",
      "val Loss: 0.5503 Acc: 0.7190\n",
      "has spend time 94m 21s/n\n",
      "\n",
      "Epoch 2655/9999\n",
      "----------\n",
      "train Loss: 0.5018 Acc: 0.7377\n",
      "has spend time 94m 23s/n\n",
      "val Loss: 0.5589 Acc: 0.7059\n",
      "has spend time 94m 23s/n\n",
      "\n",
      "Epoch 2656/9999\n",
      "----------\n",
      "train Loss: 0.5307 Acc: 0.7090\n",
      "has spend time 94m 25s/n\n",
      "val Loss: 0.5611 Acc: 0.6928\n",
      "has spend time 94m 25s/n\n",
      "\n",
      "Epoch 2657/9999\n",
      "----------\n",
      "train Loss: 0.4823 Acc: 0.7664\n",
      "has spend time 94m 27s/n\n",
      "val Loss: 0.5564 Acc: 0.6993\n",
      "has spend time 94m 27s/n\n",
      "\n",
      "Epoch 2658/9999\n",
      "----------\n",
      "train Loss: 0.4510 Acc: 0.7910\n",
      "has spend time 94m 29s/n\n",
      "val Loss: 0.5565 Acc: 0.6797\n",
      "has spend time 94m 29s/n\n",
      "\n",
      "Epoch 2659/9999\n",
      "----------\n",
      "train Loss: 0.5458 Acc: 0.7172\n",
      "has spend time 94m 31s/n\n",
      "val Loss: 0.5501 Acc: 0.7124\n",
      "has spend time 94m 32s/n\n",
      "\n",
      "Epoch 2660/9999\n",
      "----------\n",
      "train Loss: 0.5179 Acc: 0.7336\n",
      "has spend time 94m 33s/n\n",
      "val Loss: 0.5529 Acc: 0.7190\n",
      "has spend time 94m 34s/n\n",
      "\n",
      "Epoch 2661/9999\n",
      "----------\n",
      "train Loss: 0.4860 Acc: 0.7541\n",
      "has spend time 94m 36s/n\n",
      "val Loss: 0.5575 Acc: 0.7124\n",
      "has spend time 94m 36s/n\n",
      "\n",
      "Epoch 2662/9999\n",
      "----------\n",
      "train Loss: 0.5120 Acc: 0.7582\n",
      "has spend time 94m 38s/n\n",
      "val Loss: 0.5535 Acc: 0.7059\n",
      "has spend time 94m 39s/n\n",
      "\n",
      "Epoch 2663/9999\n",
      "----------\n",
      "train Loss: 0.4869 Acc: 0.7500\n",
      "has spend time 94m 40s/n\n",
      "val Loss: 0.5508 Acc: 0.6993\n",
      "has spend time 94m 41s/n\n",
      "\n",
      "Epoch 2664/9999\n",
      "----------\n",
      "train Loss: 0.5191 Acc: 0.7254\n",
      "has spend time 94m 42s/n\n",
      "val Loss: 0.5451 Acc: 0.7059\n",
      "has spend time 94m 43s/n\n",
      "\n",
      "Epoch 2665/9999\n",
      "----------\n",
      "train Loss: 0.5342 Acc: 0.7213\n",
      "has spend time 94m 44s/n\n",
      "val Loss: 0.5403 Acc: 0.7190\n",
      "has spend time 94m 45s/n\n",
      "\n",
      "Epoch 2666/9999\n",
      "----------\n",
      "train Loss: 0.5219 Acc: 0.7213\n",
      "has spend time 94m 46s/n\n",
      "val Loss: 0.5454 Acc: 0.7124\n",
      "has spend time 94m 47s/n\n",
      "\n",
      "Epoch 2667/9999\n",
      "----------\n",
      "train Loss: 0.5106 Acc: 0.7295\n",
      "has spend time 94m 48s/n\n",
      "val Loss: 0.5469 Acc: 0.6993\n",
      "has spend time 94m 49s/n\n",
      "\n",
      "Epoch 2668/9999\n",
      "----------\n",
      "train Loss: 0.5365 Acc: 0.7008\n",
      "has spend time 94m 51s/n\n",
      "val Loss: 0.5666 Acc: 0.6993\n",
      "has spend time 94m 51s/n\n",
      "\n",
      "Epoch 2669/9999\n",
      "----------\n",
      "train Loss: 0.5197 Acc: 0.7336\n",
      "has spend time 94m 53s/n\n",
      "val Loss: 0.5396 Acc: 0.7124\n",
      "has spend time 94m 53s/n\n",
      "\n",
      "Epoch 2670/9999\n",
      "----------\n",
      "train Loss: 0.4963 Acc: 0.7910\n",
      "has spend time 94m 55s/n\n",
      "val Loss: 0.5468 Acc: 0.7255\n",
      "has spend time 94m 55s/n\n",
      "\n",
      "Epoch 2671/9999\n",
      "----------\n",
      "train Loss: 0.4978 Acc: 0.7295\n",
      "has spend time 94m 57s/n\n",
      "val Loss: 0.5497 Acc: 0.7124\n",
      "has spend time 94m 57s/n\n",
      "\n",
      "Epoch 2672/9999\n",
      "----------\n",
      "train Loss: 0.4920 Acc: 0.7705\n",
      "has spend time 94m 59s/n\n",
      "val Loss: 0.5484 Acc: 0.7124\n",
      "has spend time 94m 60s/n\n",
      "\n",
      "Epoch 2673/9999\n",
      "----------\n",
      "train Loss: 0.4898 Acc: 0.7828\n",
      "has spend time 95m 1s/n\n",
      "val Loss: 0.5533 Acc: 0.7124\n",
      "has spend time 95m 2s/n\n",
      "\n",
      "Epoch 2674/9999\n",
      "----------\n",
      "train Loss: 0.5052 Acc: 0.7295\n",
      "has spend time 95m 3s/n\n",
      "val Loss: 0.5659 Acc: 0.7059\n",
      "has spend time 95m 4s/n\n",
      "\n",
      "Epoch 2675/9999\n",
      "----------\n",
      "train Loss: 0.5202 Acc: 0.7336\n",
      "has spend time 95m 5s/n\n",
      "val Loss: 0.5497 Acc: 0.7124\n",
      "has spend time 95m 6s/n\n",
      "\n",
      "Epoch 2676/9999\n",
      "----------\n",
      "train Loss: 0.5166 Acc: 0.7295\n",
      "has spend time 95m 7s/n\n",
      "val Loss: 0.5635 Acc: 0.7059\n",
      "has spend time 95m 8s/n\n",
      "\n",
      "Epoch 2677/9999\n",
      "----------\n",
      "train Loss: 0.4895 Acc: 0.7500\n",
      "has spend time 95m 10s/n\n",
      "val Loss: 0.5879 Acc: 0.6993\n",
      "has spend time 95m 10s/n\n",
      "\n",
      "Epoch 2678/9999\n",
      "----------\n",
      "train Loss: 0.5239 Acc: 0.7254\n",
      "has spend time 95m 12s/n\n",
      "val Loss: 0.5574 Acc: 0.6993\n",
      "has spend time 95m 13s/n\n",
      "\n",
      "Epoch 2679/9999\n",
      "----------\n",
      "train Loss: 0.5369 Acc: 0.6885\n",
      "has spend time 95m 14s/n\n",
      "val Loss: 0.5535 Acc: 0.6993\n",
      "has spend time 95m 15s/n\n",
      "\n",
      "Epoch 2680/9999\n",
      "----------\n",
      "train Loss: 0.5170 Acc: 0.7295\n",
      "has spend time 95m 16s/n\n",
      "val Loss: 0.5657 Acc: 0.6928\n",
      "has spend time 95m 17s/n\n",
      "\n",
      "Epoch 2681/9999\n",
      "----------\n",
      "train Loss: 0.5086 Acc: 0.7172\n",
      "has spend time 95m 18s/n\n",
      "val Loss: 0.5545 Acc: 0.7124\n",
      "has spend time 95m 19s/n\n",
      "\n",
      "Epoch 2682/9999\n",
      "----------\n",
      "train Loss: 0.4806 Acc: 0.7541\n",
      "has spend time 95m 20s/n\n",
      "val Loss: 0.5481 Acc: 0.7124\n",
      "has spend time 95m 21s/n\n",
      "\n",
      "Epoch 2683/9999\n",
      "----------\n",
      "train Loss: 0.5193 Acc: 0.7336\n",
      "has spend time 95m 22s/n\n",
      "val Loss: 0.5476 Acc: 0.7124\n",
      "has spend time 95m 23s/n\n",
      "\n",
      "Epoch 2684/9999\n",
      "----------\n",
      "train Loss: 0.5502 Acc: 0.7090\n",
      "has spend time 95m 25s/n\n",
      "val Loss: 0.5454 Acc: 0.7190\n",
      "has spend time 95m 25s/n\n",
      "\n",
      "Epoch 2685/9999\n",
      "----------\n",
      "train Loss: 0.4889 Acc: 0.7254\n",
      "has spend time 95m 27s/n\n",
      "val Loss: 0.5445 Acc: 0.7124\n",
      "has spend time 95m 27s/n\n",
      "\n",
      "Epoch 2686/9999\n",
      "----------\n",
      "train Loss: 0.5083 Acc: 0.7049\n",
      "has spend time 95m 29s/n\n",
      "val Loss: 0.5480 Acc: 0.7124\n",
      "has spend time 95m 29s/n\n",
      "\n",
      "Epoch 2687/9999\n",
      "----------\n",
      "train Loss: 0.4853 Acc: 0.7787\n",
      "has spend time 95m 31s/n\n",
      "val Loss: 0.5494 Acc: 0.7255\n",
      "has spend time 95m 31s/n\n",
      "\n",
      "Epoch 2688/9999\n",
      "----------\n",
      "train Loss: 0.5297 Acc: 0.7254\n",
      "has spend time 95m 33s/n\n",
      "val Loss: 0.5501 Acc: 0.7059\n",
      "has spend time 95m 33s/n\n",
      "\n",
      "Epoch 2689/9999\n",
      "----------\n",
      "train Loss: 0.5540 Acc: 0.7131\n",
      "has spend time 95m 35s/n\n",
      "val Loss: 0.5453 Acc: 0.7190\n",
      "has spend time 95m 36s/n\n",
      "\n",
      "Epoch 2690/9999\n",
      "----------\n",
      "train Loss: 0.5285 Acc: 0.7336\n",
      "has spend time 95m 37s/n\n",
      "val Loss: 0.5554 Acc: 0.7059\n",
      "has spend time 95m 38s/n\n",
      "\n",
      "Epoch 2691/9999\n",
      "----------\n",
      "train Loss: 0.5161 Acc: 0.7295\n",
      "has spend time 95m 40s/n\n",
      "val Loss: 0.5485 Acc: 0.7059\n",
      "has spend time 95m 40s/n\n",
      "\n",
      "Epoch 2692/9999\n",
      "----------\n",
      "train Loss: 0.4964 Acc: 0.7500\n",
      "has spend time 95m 42s/n\n",
      "val Loss: 0.5525 Acc: 0.7190\n",
      "has spend time 95m 43s/n\n",
      "\n",
      "Epoch 2693/9999\n",
      "----------\n",
      "train Loss: 0.5037 Acc: 0.7336\n",
      "has spend time 95m 44s/n\n",
      "val Loss: 0.5577 Acc: 0.7124\n",
      "has spend time 95m 45s/n\n",
      "\n",
      "Epoch 2694/9999\n",
      "----------\n",
      "train Loss: 0.4990 Acc: 0.7418\n",
      "has spend time 95m 46s/n\n",
      "val Loss: 0.5476 Acc: 0.7124\n",
      "has spend time 95m 47s/n\n",
      "\n",
      "Epoch 2695/9999\n",
      "----------\n",
      "train Loss: 0.4929 Acc: 0.7623\n",
      "has spend time 95m 48s/n\n",
      "val Loss: 0.5423 Acc: 0.7190\n",
      "has spend time 95m 49s/n\n",
      "\n",
      "Epoch 2696/9999\n",
      "----------\n",
      "train Loss: 0.5020 Acc: 0.7459\n",
      "has spend time 95m 50s/n\n",
      "val Loss: 0.5557 Acc: 0.6928\n",
      "has spend time 95m 51s/n\n",
      "\n",
      "Epoch 2697/9999\n",
      "----------\n",
      "train Loss: 0.4962 Acc: 0.7459\n",
      "has spend time 95m 53s/n\n",
      "val Loss: 0.5471 Acc: 0.7059\n",
      "has spend time 95m 53s/n\n",
      "\n",
      "Epoch 2698/9999\n",
      "----------\n",
      "train Loss: 0.5221 Acc: 0.7336\n",
      "has spend time 95m 55s/n\n",
      "val Loss: 0.5560 Acc: 0.7059\n",
      "has spend time 95m 55s/n\n",
      "\n",
      "Epoch 2699/9999\n",
      "----------\n",
      "train Loss: 0.5132 Acc: 0.7377\n",
      "has spend time 95m 57s/n\n",
      "val Loss: 0.5528 Acc: 0.7059\n",
      "has spend time 95m 57s/n\n",
      "\n",
      "Epoch 2700/9999\n",
      "----------\n",
      "train Loss: 0.5087 Acc: 0.7377\n",
      "has spend time 95m 59s/n\n",
      "val Loss: 0.5670 Acc: 0.6863\n",
      "has spend time 95m 59s/n\n",
      "\n",
      "Epoch 2701/9999\n",
      "----------\n",
      "train Loss: 0.5110 Acc: 0.7336\n",
      "has spend time 96m 1s/n\n",
      "val Loss: 0.5541 Acc: 0.7059\n",
      "has spend time 96m 1s/n\n",
      "\n",
      "Epoch 2702/9999\n",
      "----------\n",
      "train Loss: 0.5030 Acc: 0.7254\n",
      "has spend time 96m 3s/n\n",
      "val Loss: 0.5608 Acc: 0.6993\n",
      "has spend time 96m 4s/n\n",
      "\n",
      "Epoch 2703/9999\n",
      "----------\n",
      "train Loss: 0.5223 Acc: 0.7254\n",
      "has spend time 96m 5s/n\n",
      "val Loss: 0.5513 Acc: 0.6993\n",
      "has spend time 96m 6s/n\n",
      "\n",
      "Epoch 2704/9999\n",
      "----------\n",
      "train Loss: 0.5244 Acc: 0.6926\n",
      "has spend time 96m 7s/n\n",
      "val Loss: 0.5588 Acc: 0.6993\n",
      "has spend time 96m 8s/n\n",
      "\n",
      "Epoch 2705/9999\n",
      "----------\n",
      "train Loss: 0.5100 Acc: 0.7500\n",
      "has spend time 96m 9s/n\n",
      "val Loss: 0.5504 Acc: 0.6928\n",
      "has spend time 96m 10s/n\n",
      "\n",
      "Epoch 2706/9999\n",
      "----------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train Loss: 0.5015 Acc: 0.7131\n",
      "has spend time 96m 11s/n\n",
      "val Loss: 0.5548 Acc: 0.6993\n",
      "has spend time 96m 12s/n\n",
      "\n",
      "Epoch 2707/9999\n",
      "----------\n",
      "train Loss: 0.4930 Acc: 0.7418\n",
      "has spend time 96m 13s/n\n",
      "val Loss: 0.5654 Acc: 0.6993\n",
      "has spend time 96m 14s/n\n",
      "\n",
      "Epoch 2708/9999\n",
      "----------\n",
      "train Loss: 0.5035 Acc: 0.7459\n",
      "has spend time 96m 16s/n\n",
      "val Loss: 0.5560 Acc: 0.7059\n",
      "has spend time 96m 16s/n\n",
      "\n",
      "Epoch 2709/9999\n",
      "----------\n",
      "train Loss: 0.4832 Acc: 0.7541\n",
      "has spend time 96m 18s/n\n",
      "val Loss: 0.5554 Acc: 0.7124\n",
      "has spend time 96m 18s/n\n",
      "\n",
      "Epoch 2710/9999\n",
      "----------\n",
      "train Loss: 0.4757 Acc: 0.7459\n",
      "has spend time 96m 20s/n\n",
      "val Loss: 0.5531 Acc: 0.7124\n",
      "has spend time 96m 20s/n\n",
      "\n",
      "Epoch 2711/9999\n",
      "----------\n",
      "train Loss: 0.5099 Acc: 0.7336\n",
      "has spend time 96m 22s/n\n",
      "val Loss: 0.5427 Acc: 0.7124\n",
      "has spend time 96m 22s/n\n",
      "\n",
      "Epoch 2712/9999\n",
      "----------\n",
      "train Loss: 0.5155 Acc: 0.7746\n",
      "has spend time 96m 24s/n\n",
      "val Loss: 0.5431 Acc: 0.7190\n",
      "has spend time 96m 24s/n\n",
      "\n",
      "Epoch 2713/9999\n",
      "----------\n",
      "train Loss: 0.4917 Acc: 0.7705\n",
      "has spend time 96m 26s/n\n",
      "val Loss: 0.5450 Acc: 0.7190\n",
      "has spend time 96m 26s/n\n",
      "\n",
      "Epoch 2714/9999\n",
      "----------\n",
      "train Loss: 0.5161 Acc: 0.7500\n",
      "has spend time 96m 28s/n\n",
      "val Loss: 0.5471 Acc: 0.7124\n",
      "has spend time 96m 28s/n\n",
      "\n",
      "Epoch 2715/9999\n",
      "----------\n",
      "train Loss: 0.5150 Acc: 0.7090\n",
      "has spend time 96m 30s/n\n",
      "val Loss: 0.5501 Acc: 0.7190\n",
      "has spend time 96m 30s/n\n",
      "\n",
      "Epoch 2716/9999\n",
      "----------\n",
      "train Loss: 0.5097 Acc: 0.7541\n",
      "has spend time 96m 32s/n\n",
      "val Loss: 0.5550 Acc: 0.7190\n",
      "has spend time 96m 32s/n\n",
      "\n",
      "Epoch 2717/9999\n",
      "----------\n",
      "train Loss: 0.5084 Acc: 0.7459\n",
      "has spend time 96m 34s/n\n",
      "val Loss: 0.5473 Acc: 0.7124\n",
      "has spend time 96m 34s/n\n",
      "\n",
      "Epoch 2718/9999\n",
      "----------\n",
      "train Loss: 0.5403 Acc: 0.6926\n",
      "has spend time 96m 36s/n\n",
      "val Loss: 0.5493 Acc: 0.7059\n",
      "has spend time 96m 36s/n\n",
      "\n",
      "Epoch 2719/9999\n",
      "----------\n",
      "train Loss: 0.5193 Acc: 0.7295\n",
      "has spend time 96m 38s/n\n",
      "val Loss: 0.5439 Acc: 0.7255\n",
      "has spend time 96m 39s/n\n",
      "\n",
      "Epoch 2720/9999\n",
      "----------\n",
      "train Loss: 0.5090 Acc: 0.7582\n",
      "has spend time 96m 40s/n\n",
      "val Loss: 0.5558 Acc: 0.7190\n",
      "has spend time 96m 41s/n\n",
      "\n",
      "Epoch 2721/9999\n",
      "----------\n",
      "train Loss: 0.5218 Acc: 0.7295\n",
      "has spend time 96m 42s/n\n",
      "val Loss: 0.5556 Acc: 0.6863\n",
      "has spend time 96m 43s/n\n",
      "\n",
      "Epoch 2722/9999\n",
      "----------\n",
      "train Loss: 0.5140 Acc: 0.6926\n",
      "has spend time 96m 44s/n\n",
      "val Loss: 0.5545 Acc: 0.6993\n",
      "has spend time 96m 45s/n\n",
      "\n",
      "Epoch 2723/9999\n",
      "----------\n",
      "train Loss: 0.5152 Acc: 0.7377\n",
      "has spend time 96m 47s/n\n",
      "val Loss: 0.5531 Acc: 0.7124\n",
      "has spend time 96m 47s/n\n",
      "\n",
      "Epoch 2724/9999\n",
      "----------\n",
      "train Loss: 0.5105 Acc: 0.7336\n",
      "has spend time 96m 49s/n\n",
      "val Loss: 0.5506 Acc: 0.7059\n",
      "has spend time 96m 50s/n\n",
      "\n",
      "Epoch 2725/9999\n",
      "----------\n",
      "train Loss: 0.5228 Acc: 0.7213\n",
      "has spend time 96m 51s/n\n",
      "val Loss: 0.5477 Acc: 0.7059\n",
      "has spend time 96m 52s/n\n",
      "\n",
      "Epoch 2726/9999\n",
      "----------\n",
      "train Loss: 0.5244 Acc: 0.7213\n",
      "has spend time 96m 53s/n\n",
      "val Loss: 0.5580 Acc: 0.6863\n",
      "has spend time 96m 54s/n\n",
      "\n",
      "Epoch 2727/9999\n",
      "----------\n",
      "train Loss: 0.5091 Acc: 0.7664\n",
      "has spend time 96m 55s/n\n",
      "val Loss: 0.5552 Acc: 0.7059\n",
      "has spend time 96m 56s/n\n",
      "\n",
      "Epoch 2728/9999\n",
      "----------\n",
      "train Loss: 0.4978 Acc: 0.7582\n",
      "has spend time 96m 57s/n\n",
      "val Loss: 0.5508 Acc: 0.6928\n",
      "has spend time 96m 58s/n\n",
      "\n",
      "Epoch 2729/9999\n",
      "----------\n",
      "train Loss: 0.5030 Acc: 0.7254\n",
      "has spend time 96m 59s/n\n",
      "val Loss: 0.5578 Acc: 0.6993\n",
      "has spend time 96m 60s/n\n",
      "\n",
      "Epoch 2730/9999\n",
      "----------\n",
      "train Loss: 0.5353 Acc: 0.6967\n",
      "has spend time 97m 1s/n\n",
      "val Loss: 0.5552 Acc: 0.7059\n",
      "has spend time 97m 2s/n\n",
      "\n",
      "Epoch 2731/9999\n",
      "----------\n",
      "train Loss: 0.4937 Acc: 0.7418\n",
      "has spend time 97m 4s/n\n",
      "val Loss: 0.5641 Acc: 0.6928\n",
      "has spend time 97m 4s/n\n",
      "\n",
      "Epoch 2732/9999\n",
      "----------\n",
      "train Loss: 0.5199 Acc: 0.7377\n",
      "has spend time 97m 6s/n\n",
      "val Loss: 0.5478 Acc: 0.7190\n",
      "has spend time 97m 6s/n\n",
      "\n",
      "Epoch 2733/9999\n",
      "----------\n",
      "train Loss: 0.4986 Acc: 0.7746\n",
      "has spend time 97m 8s/n\n",
      "val Loss: 0.5469 Acc: 0.7124\n",
      "has spend time 97m 8s/n\n",
      "\n",
      "Epoch 2734/9999\n",
      "----------\n",
      "train Loss: 0.5167 Acc: 0.7295\n",
      "has spend time 97m 10s/n\n",
      "val Loss: 0.5467 Acc: 0.6993\n",
      "has spend time 97m 10s/n\n",
      "\n",
      "Epoch 2735/9999\n",
      "----------\n",
      "train Loss: 0.5314 Acc: 0.7377\n",
      "has spend time 97m 12s/n\n",
      "val Loss: 0.5474 Acc: 0.7124\n",
      "has spend time 97m 12s/n\n",
      "\n",
      "Epoch 2736/9999\n",
      "----------\n",
      "train Loss: 0.5024 Acc: 0.7459\n",
      "has spend time 97m 14s/n\n",
      "val Loss: 0.5493 Acc: 0.7124\n",
      "has spend time 97m 15s/n\n",
      "\n",
      "Epoch 2737/9999\n",
      "----------\n",
      "train Loss: 0.4895 Acc: 0.7541\n",
      "has spend time 97m 16s/n\n",
      "val Loss: 0.5552 Acc: 0.7124\n",
      "has spend time 97m 17s/n\n",
      "\n",
      "Epoch 2738/9999\n",
      "----------\n",
      "train Loss: 0.4893 Acc: 0.7541\n",
      "has spend time 97m 18s/n\n",
      "val Loss: 0.5550 Acc: 0.6993\n",
      "has spend time 97m 19s/n\n",
      "\n",
      "Epoch 2739/9999\n",
      "----------\n",
      "train Loss: 0.5125 Acc: 0.7295\n",
      "has spend time 97m 21s/n\n",
      "val Loss: 0.5519 Acc: 0.7059\n",
      "has spend time 97m 21s/n\n",
      "\n",
      "Epoch 2740/9999\n",
      "----------\n",
      "train Loss: 0.5068 Acc: 0.7623\n",
      "has spend time 97m 23s/n\n",
      "val Loss: 0.5471 Acc: 0.7124\n",
      "has spend time 97m 23s/n\n",
      "\n",
      "Epoch 2741/9999\n",
      "----------\n",
      "train Loss: 0.5146 Acc: 0.7541\n",
      "has spend time 97m 25s/n\n",
      "val Loss: 0.5657 Acc: 0.6993\n",
      "has spend time 97m 25s/n\n",
      "\n",
      "Epoch 2742/9999\n",
      "----------\n",
      "train Loss: 0.5493 Acc: 0.7049\n",
      "has spend time 97m 27s/n\n",
      "val Loss: 0.5541 Acc: 0.6928\n",
      "has spend time 97m 28s/n\n",
      "\n",
      "Epoch 2743/9999\n",
      "----------\n",
      "train Loss: 0.5125 Acc: 0.7213\n",
      "has spend time 97m 29s/n\n",
      "val Loss: 0.5580 Acc: 0.6863\n",
      "has spend time 97m 30s/n\n",
      "\n",
      "Epoch 2744/9999\n",
      "----------\n",
      "train Loss: 0.5028 Acc: 0.7705\n",
      "has spend time 97m 32s/n\n",
      "val Loss: 0.5490 Acc: 0.7059\n",
      "has spend time 97m 32s/n\n",
      "\n",
      "Epoch 2745/9999\n",
      "----------\n",
      "train Loss: 0.5394 Acc: 0.7049\n",
      "has spend time 97m 34s/n\n",
      "val Loss: 0.5593 Acc: 0.7059\n",
      "has spend time 97m 35s/n\n",
      "\n",
      "Epoch 2746/9999\n",
      "----------\n",
      "train Loss: 0.4916 Acc: 0.7582\n",
      "has spend time 97m 36s/n\n",
      "val Loss: 0.5562 Acc: 0.7059\n",
      "has spend time 97m 37s/n\n",
      "\n",
      "Epoch 2747/9999\n",
      "----------\n",
      "train Loss: 0.5003 Acc: 0.7500\n",
      "has spend time 97m 38s/n\n",
      "val Loss: 0.5475 Acc: 0.7255\n",
      "has spend time 97m 39s/n\n",
      "\n",
      "Epoch 2748/9999\n",
      "----------\n",
      "train Loss: 0.5165 Acc: 0.7254\n",
      "has spend time 97m 40s/n\n",
      "val Loss: 0.5475 Acc: 0.7124\n",
      "has spend time 97m 41s/n\n",
      "\n",
      "Epoch 2749/9999\n",
      "----------\n",
      "train Loss: 0.5404 Acc: 0.7336\n",
      "has spend time 97m 43s/n\n",
      "val Loss: 0.5505 Acc: 0.7059\n",
      "has spend time 97m 43s/n\n",
      "\n",
      "Epoch 2750/9999\n",
      "----------\n",
      "train Loss: 0.5169 Acc: 0.7131\n",
      "has spend time 97m 45s/n\n",
      "val Loss: 0.5506 Acc: 0.6993\n",
      "has spend time 97m 45s/n\n",
      "\n",
      "Epoch 2751/9999\n",
      "----------\n",
      "train Loss: 0.4758 Acc: 0.7869\n",
      "has spend time 97m 47s/n\n",
      "val Loss: 0.5748 Acc: 0.6993\n",
      "has spend time 97m 48s/n\n",
      "\n",
      "Epoch 2752/9999\n",
      "----------\n",
      "train Loss: 0.5059 Acc: 0.7582\n",
      "has spend time 97m 49s/n\n",
      "val Loss: 0.5532 Acc: 0.7059\n",
      "has spend time 97m 50s/n\n",
      "\n",
      "Epoch 2753/9999\n",
      "----------\n",
      "train Loss: 0.5163 Acc: 0.6967\n",
      "has spend time 97m 51s/n\n",
      "val Loss: 0.5616 Acc: 0.6993\n",
      "has spend time 97m 52s/n\n",
      "\n",
      "Epoch 2754/9999\n",
      "----------\n",
      "train Loss: 0.5085 Acc: 0.7500\n",
      "has spend time 97m 53s/n\n",
      "val Loss: 0.5503 Acc: 0.7255\n",
      "has spend time 97m 54s/n\n",
      "\n",
      "Epoch 2755/9999\n",
      "----------\n",
      "train Loss: 0.5000 Acc: 0.7418\n",
      "has spend time 97m 56s/n\n",
      "val Loss: 0.5539 Acc: 0.7059\n",
      "has spend time 97m 56s/n\n",
      "\n",
      "Epoch 2756/9999\n",
      "----------\n",
      "train Loss: 0.5232 Acc: 0.7459\n",
      "has spend time 97m 58s/n\n",
      "val Loss: 0.5494 Acc: 0.6993\n",
      "has spend time 97m 58s/n\n",
      "\n",
      "Epoch 2757/9999\n",
      "----------\n",
      "train Loss: 0.4846 Acc: 0.7377\n",
      "has spend time 97m 60s/n\n",
      "val Loss: 0.5565 Acc: 0.6863\n",
      "has spend time 98m 1s/n\n",
      "\n",
      "Epoch 2758/9999\n",
      "----------\n",
      "train Loss: 0.5171 Acc: 0.7131\n",
      "has spend time 98m 2s/n\n",
      "val Loss: 0.5436 Acc: 0.7124\n",
      "has spend time 98m 3s/n\n",
      "\n",
      "Epoch 2759/9999\n",
      "----------\n",
      "train Loss: 0.4843 Acc: 0.7746\n",
      "has spend time 98m 4s/n\n",
      "val Loss: 0.5446 Acc: 0.7255\n",
      "has spend time 98m 5s/n\n",
      "\n",
      "Epoch 2760/9999\n",
      "----------\n",
      "train Loss: 0.5112 Acc: 0.7418\n",
      "has spend time 98m 6s/n\n",
      "val Loss: 0.5658 Acc: 0.6993\n",
      "has spend time 98m 7s/n\n",
      "\n",
      "Epoch 2761/9999\n",
      "----------\n",
      "train Loss: 0.5292 Acc: 0.7049\n",
      "has spend time 98m 8s/n\n",
      "val Loss: 0.5560 Acc: 0.7124\n",
      "has spend time 98m 9s/n\n",
      "\n",
      "Epoch 2762/9999\n",
      "----------\n",
      "train Loss: 0.4858 Acc: 0.7582\n",
      "has spend time 98m 10s/n\n",
      "val Loss: 0.5544 Acc: 0.7190\n",
      "has spend time 98m 11s/n\n",
      "\n",
      "Epoch 2763/9999\n",
      "----------\n",
      "train Loss: 0.5096 Acc: 0.7418\n",
      "has spend time 98m 12s/n\n",
      "val Loss: 0.5467 Acc: 0.6993\n",
      "has spend time 98m 13s/n\n",
      "\n",
      "Epoch 2764/9999\n",
      "----------\n",
      "train Loss: 0.5154 Acc: 0.7295\n",
      "has spend time 98m 14s/n\n",
      "val Loss: 0.5422 Acc: 0.7255\n",
      "has spend time 98m 15s/n\n",
      "\n",
      "Epoch 2765/9999\n",
      "----------\n",
      "train Loss: 0.5134 Acc: 0.7418\n",
      "has spend time 98m 17s/n\n",
      "val Loss: 0.5584 Acc: 0.6928\n",
      "has spend time 98m 17s/n\n",
      "\n",
      "Epoch 2766/9999\n",
      "----------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train Loss: 0.5016 Acc: 0.7295\n",
      "has spend time 98m 19s/n\n",
      "val Loss: 0.5516 Acc: 0.7059\n",
      "has spend time 98m 19s/n\n",
      "\n",
      "Epoch 2767/9999\n",
      "----------\n",
      "train Loss: 0.5252 Acc: 0.7459\n",
      "has spend time 98m 21s/n\n",
      "val Loss: 0.5729 Acc: 0.6993\n",
      "has spend time 98m 21s/n\n",
      "\n",
      "Epoch 2768/9999\n",
      "----------\n",
      "train Loss: 0.4977 Acc: 0.7623\n",
      "has spend time 98m 23s/n\n",
      "val Loss: 0.5556 Acc: 0.7059\n",
      "has spend time 98m 23s/n\n",
      "\n",
      "Epoch 2769/9999\n",
      "----------\n",
      "train Loss: 0.5359 Acc: 0.7295\n",
      "has spend time 98m 25s/n\n",
      "val Loss: 0.5584 Acc: 0.7059\n",
      "has spend time 98m 25s/n\n",
      "\n",
      "Epoch 2770/9999\n",
      "----------\n",
      "train Loss: 0.5232 Acc: 0.7213\n",
      "has spend time 98m 27s/n\n",
      "val Loss: 0.5518 Acc: 0.7059\n",
      "has spend time 98m 28s/n\n",
      "\n",
      "Epoch 2771/9999\n",
      "----------\n",
      "train Loss: 0.5132 Acc: 0.7541\n",
      "has spend time 98m 29s/n\n",
      "val Loss: 0.5433 Acc: 0.7059\n",
      "has spend time 98m 30s/n\n",
      "\n",
      "Epoch 2772/9999\n",
      "----------\n",
      "train Loss: 0.5018 Acc: 0.7664\n",
      "has spend time 98m 31s/n\n",
      "val Loss: 0.5521 Acc: 0.7124\n",
      "has spend time 98m 32s/n\n",
      "\n",
      "Epoch 2773/9999\n",
      "----------\n",
      "train Loss: 0.5090 Acc: 0.7664\n",
      "has spend time 98m 34s/n\n",
      "val Loss: 0.5535 Acc: 0.7190\n",
      "has spend time 98m 34s/n\n",
      "\n",
      "Epoch 2774/9999\n",
      "----------\n",
      "train Loss: 0.4982 Acc: 0.7377\n",
      "has spend time 98m 36s/n\n",
      "val Loss: 0.5513 Acc: 0.7059\n",
      "has spend time 98m 37s/n\n",
      "\n",
      "Epoch 2775/9999\n",
      "----------\n",
      "train Loss: 0.5074 Acc: 0.7459\n",
      "has spend time 98m 38s/n\n",
      "val Loss: 0.5506 Acc: 0.6928\n",
      "has spend time 98m 39s/n\n",
      "\n",
      "Epoch 2776/9999\n",
      "----------\n",
      "train Loss: 0.5156 Acc: 0.7049\n",
      "has spend time 98m 40s/n\n",
      "val Loss: 0.5392 Acc: 0.7190\n",
      "has spend time 98m 41s/n\n",
      "\n",
      "Epoch 2777/9999\n",
      "----------\n",
      "train Loss: 0.4728 Acc: 0.7828\n",
      "has spend time 98m 42s/n\n",
      "val Loss: 0.5500 Acc: 0.7059\n",
      "has spend time 98m 43s/n\n",
      "\n",
      "Epoch 2778/9999\n",
      "----------\n",
      "train Loss: 0.4822 Acc: 0.7295\n",
      "has spend time 98m 44s/n\n",
      "val Loss: 0.5545 Acc: 0.6797\n",
      "has spend time 98m 45s/n\n",
      "\n",
      "Epoch 2779/9999\n",
      "----------\n",
      "train Loss: 0.5002 Acc: 0.7295\n",
      "has spend time 98m 46s/n\n",
      "val Loss: 0.5516 Acc: 0.7124\n",
      "has spend time 98m 47s/n\n",
      "\n",
      "Epoch 2780/9999\n",
      "----------\n",
      "train Loss: 0.5022 Acc: 0.7377\n",
      "has spend time 98m 49s/n\n",
      "val Loss: 0.5501 Acc: 0.7124\n",
      "has spend time 98m 49s/n\n",
      "\n",
      "Epoch 2781/9999\n",
      "----------\n",
      "train Loss: 0.5293 Acc: 0.7172\n",
      "has spend time 98m 51s/n\n",
      "val Loss: 0.5476 Acc: 0.6993\n",
      "has spend time 98m 52s/n\n",
      "\n",
      "Epoch 2782/9999\n",
      "----------\n",
      "train Loss: 0.5028 Acc: 0.7377\n",
      "has spend time 98m 54s/n\n",
      "val Loss: 0.5464 Acc: 0.7059\n",
      "has spend time 98m 54s/n\n",
      "\n",
      "Epoch 2783/9999\n",
      "----------\n",
      "train Loss: 0.5123 Acc: 0.7172\n",
      "has spend time 98m 56s/n\n",
      "val Loss: 0.5445 Acc: 0.7190\n",
      "has spend time 98m 56s/n\n",
      "\n",
      "Epoch 2784/9999\n",
      "----------\n",
      "train Loss: 0.5030 Acc: 0.7295\n",
      "has spend time 98m 58s/n\n",
      "val Loss: 0.5543 Acc: 0.7059\n",
      "has spend time 98m 58s/n\n",
      "\n",
      "Epoch 2785/9999\n",
      "----------\n",
      "train Loss: 0.5087 Acc: 0.7418\n",
      "has spend time 98m 60s/n\n",
      "val Loss: 0.5637 Acc: 0.6928\n",
      "has spend time 99m 0s/n\n",
      "\n",
      "Epoch 2786/9999\n",
      "----------\n",
      "train Loss: 0.4951 Acc: 0.7336\n",
      "has spend time 99m 2s/n\n",
      "val Loss: 0.5416 Acc: 0.7255\n",
      "has spend time 99m 2s/n\n",
      "\n",
      "Epoch 2787/9999\n",
      "----------\n",
      "train Loss: 0.5070 Acc: 0.7172\n",
      "has spend time 99m 4s/n\n",
      "val Loss: 0.5488 Acc: 0.7124\n",
      "has spend time 99m 5s/n\n",
      "\n",
      "Epoch 2788/9999\n",
      "----------\n",
      "train Loss: 0.5067 Acc: 0.7377\n",
      "has spend time 99m 6s/n\n",
      "val Loss: 0.5587 Acc: 0.6993\n",
      "has spend time 99m 7s/n\n",
      "\n",
      "Epoch 2789/9999\n",
      "----------\n",
      "train Loss: 0.5172 Acc: 0.7090\n",
      "has spend time 99m 8s/n\n",
      "val Loss: 0.5639 Acc: 0.6863\n",
      "has spend time 99m 9s/n\n",
      "\n",
      "Epoch 2790/9999\n",
      "----------\n",
      "train Loss: 0.5214 Acc: 0.7418\n",
      "has spend time 99m 11s/n\n",
      "val Loss: 0.5503 Acc: 0.7124\n",
      "has spend time 99m 11s/n\n",
      "\n",
      "Epoch 2791/9999\n",
      "----------\n",
      "train Loss: 0.4908 Acc: 0.7746\n",
      "has spend time 99m 13s/n\n",
      "val Loss: 0.5477 Acc: 0.7124\n",
      "has spend time 99m 14s/n\n",
      "\n",
      "Epoch 2792/9999\n",
      "----------\n",
      "train Loss: 0.5145 Acc: 0.7377\n",
      "has spend time 99m 15s/n\n",
      "val Loss: 0.5491 Acc: 0.7124\n",
      "has spend time 99m 16s/n\n",
      "\n",
      "Epoch 2793/9999\n",
      "----------\n",
      "train Loss: 0.5151 Acc: 0.7254\n",
      "has spend time 99m 17s/n\n",
      "val Loss: 0.5446 Acc: 0.6993\n",
      "has spend time 99m 18s/n\n",
      "\n",
      "Epoch 2794/9999\n",
      "----------\n",
      "train Loss: 0.5289 Acc: 0.6926\n",
      "has spend time 99m 19s/n\n",
      "val Loss: 0.5441 Acc: 0.6993\n",
      "has spend time 99m 20s/n\n",
      "\n",
      "Epoch 2795/9999\n",
      "----------\n",
      "train Loss: 0.5086 Acc: 0.7295\n",
      "has spend time 99m 21s/n\n",
      "val Loss: 0.5486 Acc: 0.7124\n",
      "has spend time 99m 22s/n\n",
      "\n",
      "Epoch 2796/9999\n",
      "----------\n",
      "train Loss: 0.5309 Acc: 0.7172\n",
      "has spend time 99m 23s/n\n",
      "val Loss: 0.5540 Acc: 0.7124\n",
      "has spend time 99m 24s/n\n",
      "\n",
      "Epoch 2797/9999\n",
      "----------\n",
      "train Loss: 0.4907 Acc: 0.7500\n",
      "has spend time 99m 25s/n\n",
      "val Loss: 0.5527 Acc: 0.7059\n",
      "has spend time 99m 26s/n\n",
      "\n",
      "Epoch 2798/9999\n",
      "----------\n",
      "train Loss: 0.5156 Acc: 0.7295\n",
      "has spend time 99m 27s/n\n",
      "val Loss: 0.5525 Acc: 0.6993\n",
      "has spend time 99m 28s/n\n",
      "\n",
      "Epoch 2799/9999\n",
      "----------\n",
      "train Loss: 0.5317 Acc: 0.7336\n",
      "has spend time 99m 29s/n\n",
      "val Loss: 0.5529 Acc: 0.7059\n",
      "has spend time 99m 30s/n\n",
      "\n",
      "Epoch 2800/9999\n",
      "----------\n",
      "train Loss: 0.5092 Acc: 0.7377\n",
      "has spend time 99m 32s/n\n",
      "val Loss: 0.5483 Acc: 0.7124\n",
      "has spend time 99m 33s/n\n",
      "\n",
      "Epoch 2801/9999\n",
      "----------\n",
      "train Loss: 0.4951 Acc: 0.7459\n",
      "has spend time 99m 34s/n\n",
      "val Loss: 0.5519 Acc: 0.7124\n",
      "has spend time 99m 35s/n\n",
      "\n",
      "Epoch 2802/9999\n",
      "----------\n",
      "train Loss: 0.4865 Acc: 0.7623\n",
      "has spend time 99m 36s/n\n",
      "val Loss: 0.5468 Acc: 0.7124\n",
      "has spend time 99m 37s/n\n",
      "\n",
      "Epoch 2803/9999\n",
      "----------\n",
      "train Loss: 0.4814 Acc: 0.7664\n",
      "has spend time 99m 38s/n\n",
      "val Loss: 0.5475 Acc: 0.7124\n",
      "has spend time 99m 39s/n\n",
      "\n",
      "Epoch 2804/9999\n",
      "----------\n",
      "train Loss: 0.5240 Acc: 0.7459\n",
      "has spend time 99m 40s/n\n",
      "val Loss: 0.5544 Acc: 0.6993\n",
      "has spend time 99m 41s/n\n",
      "\n",
      "Epoch 2805/9999\n",
      "----------\n",
      "train Loss: 0.4922 Acc: 0.7295\n",
      "has spend time 99m 42s/n\n",
      "val Loss: 0.5596 Acc: 0.6928\n",
      "has spend time 99m 43s/n\n",
      "\n",
      "Epoch 2806/9999\n",
      "----------\n",
      "train Loss: 0.5072 Acc: 0.7664\n",
      "has spend time 99m 45s/n\n",
      "val Loss: 0.5494 Acc: 0.7190\n",
      "has spend time 99m 45s/n\n",
      "\n",
      "Epoch 2807/9999\n",
      "----------\n",
      "train Loss: 0.5142 Acc: 0.7377\n",
      "has spend time 99m 47s/n\n",
      "val Loss: 0.5534 Acc: 0.7124\n",
      "has spend time 99m 47s/n\n",
      "\n",
      "Epoch 2808/9999\n",
      "----------\n",
      "train Loss: 0.5073 Acc: 0.7254\n",
      "has spend time 99m 49s/n\n",
      "val Loss: 0.5476 Acc: 0.7190\n",
      "has spend time 99m 49s/n\n",
      "\n",
      "Epoch 2809/9999\n",
      "----------\n",
      "train Loss: 0.5331 Acc: 0.7377\n",
      "has spend time 99m 51s/n\n",
      "val Loss: 0.5450 Acc: 0.7059\n",
      "has spend time 99m 52s/n\n",
      "\n",
      "Epoch 2810/9999\n",
      "----------\n",
      "train Loss: 0.5222 Acc: 0.7295\n",
      "has spend time 99m 53s/n\n",
      "val Loss: 0.5535 Acc: 0.7059\n",
      "has spend time 99m 54s/n\n",
      "\n",
      "Epoch 2811/9999\n",
      "----------\n",
      "train Loss: 0.5218 Acc: 0.7336\n",
      "has spend time 99m 55s/n\n",
      "val Loss: 0.5538 Acc: 0.7190\n",
      "has spend time 99m 56s/n\n",
      "\n",
      "Epoch 2812/9999\n",
      "----------\n",
      "train Loss: 0.5523 Acc: 0.7049\n",
      "has spend time 99m 57s/n\n",
      "val Loss: 0.5423 Acc: 0.7124\n",
      "has spend time 99m 58s/n\n",
      "\n",
      "Epoch 2813/9999\n",
      "----------\n",
      "train Loss: 0.4931 Acc: 0.7254\n",
      "has spend time 99m 60s/n\n",
      "val Loss: 0.5560 Acc: 0.7059\n",
      "has spend time 100m 0s/n\n",
      "\n",
      "Epoch 2814/9999\n",
      "----------\n",
      "train Loss: 0.5214 Acc: 0.7254\n",
      "has spend time 100m 2s/n\n",
      "val Loss: 0.5599 Acc: 0.7059\n",
      "has spend time 100m 2s/n\n",
      "\n",
      "Epoch 2815/9999\n",
      "----------\n",
      "train Loss: 0.5120 Acc: 0.7582\n",
      "has spend time 100m 4s/n\n",
      "val Loss: 0.5520 Acc: 0.7059\n",
      "has spend time 100m 4s/n\n",
      "\n",
      "Epoch 2816/9999\n",
      "----------\n",
      "train Loss: 0.5314 Acc: 0.7582\n",
      "has spend time 100m 6s/n\n",
      "val Loss: 0.5420 Acc: 0.7190\n",
      "has spend time 100m 6s/n\n",
      "\n",
      "Epoch 2817/9999\n",
      "----------\n",
      "train Loss: 0.5183 Acc: 0.7254\n",
      "has spend time 100m 8s/n\n",
      "val Loss: 0.5701 Acc: 0.6797\n",
      "has spend time 100m 8s/n\n",
      "\n",
      "Epoch 2818/9999\n",
      "----------\n",
      "train Loss: 0.5169 Acc: 0.7459\n",
      "has spend time 100m 10s/n\n",
      "val Loss: 0.5482 Acc: 0.7124\n",
      "has spend time 100m 10s/n\n",
      "\n",
      "Epoch 2819/9999\n",
      "----------\n",
      "train Loss: 0.5476 Acc: 0.6803\n",
      "has spend time 100m 12s/n\n",
      "val Loss: 0.5421 Acc: 0.7190\n",
      "has spend time 100m 12s/n\n",
      "\n",
      "Epoch 2820/9999\n",
      "----------\n",
      "train Loss: 0.5267 Acc: 0.7377\n",
      "has spend time 100m 14s/n\n",
      "val Loss: 0.5441 Acc: 0.7190\n",
      "has spend time 100m 15s/n\n",
      "\n",
      "Epoch 2821/9999\n",
      "----------\n",
      "train Loss: 0.5118 Acc: 0.7541\n",
      "has spend time 100m 16s/n\n",
      "val Loss: 0.5467 Acc: 0.6993\n",
      "has spend time 100m 17s/n\n",
      "\n",
      "Epoch 2822/9999\n",
      "----------\n",
      "train Loss: 0.4976 Acc: 0.7500\n",
      "has spend time 100m 18s/n\n",
      "val Loss: 0.5463 Acc: 0.7059\n",
      "has spend time 100m 19s/n\n",
      "\n",
      "Epoch 2823/9999\n",
      "----------\n",
      "train Loss: 0.5100 Acc: 0.7377\n",
      "has spend time 100m 20s/n\n",
      "val Loss: 0.5569 Acc: 0.7124\n",
      "has spend time 100m 21s/n\n",
      "\n",
      "Epoch 2824/9999\n",
      "----------\n",
      "train Loss: 0.4814 Acc: 0.7746\n",
      "has spend time 100m 22s/n\n",
      "val Loss: 0.5517 Acc: 0.7059\n",
      "has spend time 100m 23s/n\n",
      "\n",
      "Epoch 2825/9999\n",
      "----------\n",
      "train Loss: 0.5164 Acc: 0.7459\n",
      "has spend time 100m 25s/n\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val Loss: 0.5531 Acc: 0.7059\n",
      "has spend time 100m 25s/n\n",
      "\n",
      "Epoch 2826/9999\n",
      "----------\n",
      "train Loss: 0.4609 Acc: 0.7828\n",
      "has spend time 100m 27s/n\n",
      "val Loss: 0.5491 Acc: 0.7190\n",
      "has spend time 100m 28s/n\n",
      "\n",
      "Epoch 2827/9999\n",
      "----------\n",
      "train Loss: 0.4991 Acc: 0.7377\n",
      "has spend time 100m 29s/n\n",
      "val Loss: 0.5423 Acc: 0.7255\n",
      "has spend time 100m 30s/n\n",
      "\n",
      "Epoch 2828/9999\n",
      "----------\n",
      "train Loss: 0.5049 Acc: 0.7295\n",
      "has spend time 100m 31s/n\n",
      "val Loss: 0.5565 Acc: 0.7124\n",
      "has spend time 100m 32s/n\n",
      "\n",
      "Epoch 2829/9999\n",
      "----------\n",
      "train Loss: 0.5047 Acc: 0.7582\n",
      "has spend time 100m 33s/n\n",
      "val Loss: 0.5630 Acc: 0.6928\n",
      "has spend time 100m 34s/n\n",
      "\n",
      "Epoch 2830/9999\n",
      "----------\n",
      "train Loss: 0.4994 Acc: 0.7705\n",
      "has spend time 100m 35s/n\n",
      "val Loss: 0.5786 Acc: 0.6928\n",
      "has spend time 100m 36s/n\n",
      "\n",
      "Epoch 2831/9999\n",
      "----------\n",
      "train Loss: 0.4917 Acc: 0.7377\n",
      "has spend time 100m 37s/n\n",
      "val Loss: 0.5530 Acc: 0.6993\n",
      "has spend time 100m 38s/n\n",
      "\n",
      "Epoch 2832/9999\n",
      "----------\n",
      "train Loss: 0.5040 Acc: 0.7418\n",
      "has spend time 100m 39s/n\n",
      "val Loss: 0.5453 Acc: 0.7059\n",
      "has spend time 100m 40s/n\n",
      "\n",
      "Epoch 2833/9999\n",
      "----------\n",
      "train Loss: 0.4894 Acc: 0.7582\n",
      "has spend time 100m 42s/n\n",
      "val Loss: 0.5549 Acc: 0.7124\n",
      "has spend time 100m 42s/n\n",
      "\n",
      "Epoch 2834/9999\n",
      "----------\n",
      "train Loss: 0.4889 Acc: 0.7336\n",
      "has spend time 100m 44s/n\n",
      "val Loss: 0.5453 Acc: 0.7190\n",
      "has spend time 100m 44s/n\n",
      "\n",
      "Epoch 2835/9999\n",
      "----------\n",
      "train Loss: 0.5187 Acc: 0.7336\n",
      "has spend time 100m 46s/n\n",
      "val Loss: 0.5507 Acc: 0.6993\n",
      "has spend time 100m 46s/n\n",
      "\n",
      "Epoch 2836/9999\n",
      "----------\n",
      "train Loss: 0.5173 Acc: 0.7295\n",
      "has spend time 100m 48s/n\n",
      "val Loss: 0.5477 Acc: 0.7255\n",
      "has spend time 100m 48s/n\n",
      "\n",
      "Epoch 2837/9999\n",
      "----------\n",
      "train Loss: 0.4944 Acc: 0.7664\n",
      "has spend time 100m 50s/n\n",
      "val Loss: 0.5594 Acc: 0.6993\n",
      "has spend time 100m 50s/n\n",
      "\n",
      "Epoch 2838/9999\n",
      "----------\n",
      "train Loss: 0.5192 Acc: 0.7459\n",
      "has spend time 100m 52s/n\n",
      "val Loss: 0.5505 Acc: 0.7059\n",
      "has spend time 100m 53s/n\n",
      "\n",
      "Epoch 2839/9999\n",
      "----------\n",
      "train Loss: 0.5280 Acc: 0.7213\n",
      "has spend time 100m 54s/n\n",
      "val Loss: 0.5498 Acc: 0.7059\n",
      "has spend time 100m 55s/n\n",
      "\n",
      "Epoch 2840/9999\n",
      "----------\n",
      "train Loss: 0.5034 Acc: 0.7336\n",
      "has spend time 100m 56s/n\n",
      "val Loss: 0.5555 Acc: 0.6993\n",
      "has spend time 100m 57s/n\n",
      "\n",
      "Epoch 2841/9999\n",
      "----------\n",
      "train Loss: 0.5205 Acc: 0.7131\n",
      "has spend time 100m 59s/n\n",
      "val Loss: 0.5508 Acc: 0.7124\n",
      "has spend time 100m 59s/n\n",
      "\n",
      "Epoch 2842/9999\n",
      "----------\n",
      "train Loss: 0.5194 Acc: 0.7336\n",
      "has spend time 101m 1s/n\n",
      "val Loss: 0.5580 Acc: 0.6928\n",
      "has spend time 101m 2s/n\n",
      "\n",
      "Epoch 2843/9999\n",
      "----------\n",
      "train Loss: 0.5130 Acc: 0.7336\n",
      "has spend time 101m 3s/n\n",
      "val Loss: 0.5472 Acc: 0.7190\n",
      "has spend time 101m 4s/n\n",
      "\n",
      "Epoch 2844/9999\n",
      "----------\n",
      "train Loss: 0.5048 Acc: 0.7418\n",
      "has spend time 101m 5s/n\n",
      "val Loss: 0.5441 Acc: 0.7255\n",
      "has spend time 101m 6s/n\n",
      "\n",
      "Epoch 2845/9999\n",
      "----------\n",
      "train Loss: 0.5208 Acc: 0.7295\n",
      "has spend time 101m 7s/n\n",
      "val Loss: 0.5421 Acc: 0.7190\n",
      "has spend time 101m 8s/n\n",
      "\n",
      "Epoch 2846/9999\n",
      "----------\n",
      "train Loss: 0.5146 Acc: 0.7295\n",
      "has spend time 101m 9s/n\n",
      "val Loss: 0.5433 Acc: 0.7255\n",
      "has spend time 101m 10s/n\n",
      "\n",
      "Epoch 2847/9999\n",
      "----------\n",
      "train Loss: 0.4773 Acc: 0.7746\n",
      "has spend time 101m 12s/n\n",
      "val Loss: 0.5468 Acc: 0.7190\n",
      "has spend time 101m 12s/n\n",
      "\n",
      "Epoch 2848/9999\n",
      "----------\n",
      "train Loss: 0.5167 Acc: 0.7746\n",
      "has spend time 101m 14s/n\n",
      "val Loss: 0.5630 Acc: 0.6993\n",
      "has spend time 101m 15s/n\n",
      "\n",
      "Epoch 2849/9999\n",
      "----------\n",
      "train Loss: 0.5083 Acc: 0.7705\n",
      "has spend time 101m 16s/n\n",
      "val Loss: 0.5513 Acc: 0.7059\n",
      "has spend time 101m 17s/n\n",
      "\n",
      "Epoch 2850/9999\n",
      "----------\n",
      "train Loss: 0.5128 Acc: 0.7336\n",
      "has spend time 101m 18s/n\n",
      "val Loss: 0.5406 Acc: 0.7255\n",
      "has spend time 101m 19s/n\n",
      "\n",
      "Epoch 2851/9999\n",
      "----------\n",
      "train Loss: 0.5257 Acc: 0.7295\n",
      "has spend time 101m 20s/n\n",
      "val Loss: 0.5523 Acc: 0.6928\n",
      "has spend time 101m 21s/n\n",
      "\n",
      "Epoch 2852/9999\n",
      "----------\n",
      "train Loss: 0.4914 Acc: 0.7459\n",
      "has spend time 101m 22s/n\n",
      "val Loss: 0.5556 Acc: 0.7059\n",
      "has spend time 101m 23s/n\n",
      "\n",
      "Epoch 2853/9999\n",
      "----------\n",
      "train Loss: 0.5101 Acc: 0.7418\n",
      "has spend time 101m 24s/n\n",
      "val Loss: 0.5409 Acc: 0.7124\n",
      "has spend time 101m 25s/n\n",
      "\n",
      "Epoch 2854/9999\n",
      "----------\n",
      "train Loss: 0.5029 Acc: 0.7664\n",
      "has spend time 101m 27s/n\n",
      "val Loss: 0.5681 Acc: 0.6928\n",
      "has spend time 101m 27s/n\n",
      "\n",
      "Epoch 2855/9999\n",
      "----------\n",
      "train Loss: 0.4661 Acc: 0.7951\n",
      "has spend time 101m 29s/n\n",
      "val Loss: 0.5551 Acc: 0.6993\n",
      "has spend time 101m 29s/n\n",
      "\n",
      "Epoch 2856/9999\n",
      "----------\n",
      "train Loss: 0.5214 Acc: 0.7295\n",
      "has spend time 101m 31s/n\n",
      "val Loss: 0.5523 Acc: 0.7059\n",
      "has spend time 101m 31s/n\n",
      "\n",
      "Epoch 2857/9999\n",
      "----------\n",
      "train Loss: 0.5175 Acc: 0.7172\n",
      "has spend time 101m 33s/n\n",
      "val Loss: 0.5469 Acc: 0.7059\n",
      "has spend time 101m 34s/n\n",
      "\n",
      "Epoch 2858/9999\n",
      "----------\n",
      "train Loss: 0.4980 Acc: 0.7295\n",
      "has spend time 101m 35s/n\n",
      "val Loss: 0.5485 Acc: 0.7059\n",
      "has spend time 101m 36s/n\n",
      "\n",
      "Epoch 2859/9999\n",
      "----------\n",
      "train Loss: 0.4946 Acc: 0.7418\n",
      "has spend time 101m 37s/n\n",
      "val Loss: 0.5701 Acc: 0.6928\n",
      "has spend time 101m 38s/n\n",
      "\n",
      "Epoch 2860/9999\n",
      "----------\n",
      "train Loss: 0.5151 Acc: 0.6926\n",
      "has spend time 101m 39s/n\n",
      "val Loss: 0.5624 Acc: 0.6928\n",
      "has spend time 101m 40s/n\n",
      "\n",
      "Epoch 2861/9999\n",
      "----------\n",
      "train Loss: 0.5021 Acc: 0.7213\n",
      "has spend time 101m 41s/n\n",
      "val Loss: 0.5587 Acc: 0.6993\n",
      "has spend time 101m 42s/n\n",
      "\n",
      "Epoch 2862/9999\n",
      "----------\n",
      "train Loss: 0.4949 Acc: 0.7377\n",
      "has spend time 101m 43s/n\n",
      "val Loss: 0.5441 Acc: 0.7255\n",
      "has spend time 101m 44s/n\n",
      "\n",
      "Epoch 2863/9999\n",
      "----------\n",
      "train Loss: 0.4909 Acc: 0.7582\n",
      "has spend time 101m 45s/n\n",
      "val Loss: 0.5427 Acc: 0.7190\n",
      "has spend time 101m 46s/n\n",
      "\n",
      "Epoch 2864/9999\n",
      "----------\n",
      "train Loss: 0.5075 Acc: 0.7295\n",
      "has spend time 101m 47s/n\n",
      "val Loss: 0.5547 Acc: 0.7059\n",
      "has spend time 101m 48s/n\n",
      "\n",
      "Epoch 2865/9999\n",
      "----------\n",
      "train Loss: 0.5030 Acc: 0.7828\n",
      "has spend time 101m 50s/n\n",
      "val Loss: 0.5516 Acc: 0.6928\n",
      "has spend time 101m 51s/n\n",
      "\n",
      "Epoch 2866/9999\n",
      "----------\n",
      "train Loss: 0.5217 Acc: 0.7295\n",
      "has spend time 101m 52s/n\n",
      "val Loss: 0.5582 Acc: 0.7059\n",
      "has spend time 101m 53s/n\n",
      "\n",
      "Epoch 2867/9999\n",
      "----------\n",
      "train Loss: 0.4944 Acc: 0.7131\n",
      "has spend time 101m 54s/n\n",
      "val Loss: 0.5430 Acc: 0.7059\n",
      "has spend time 101m 55s/n\n",
      "\n",
      "Epoch 2868/9999\n",
      "----------\n",
      "train Loss: 0.4937 Acc: 0.7459\n",
      "has spend time 101m 56s/n\n",
      "val Loss: 0.5610 Acc: 0.6993\n",
      "has spend time 101m 57s/n\n",
      "\n",
      "Epoch 2869/9999\n",
      "----------\n",
      "train Loss: 0.5557 Acc: 0.6803\n",
      "has spend time 101m 58s/n\n",
      "val Loss: 0.5485 Acc: 0.7124\n",
      "has spend time 101m 59s/n\n",
      "\n",
      "Epoch 2870/9999\n",
      "----------\n",
      "train Loss: 0.5022 Acc: 0.7336\n",
      "has spend time 102m 0s/n\n",
      "val Loss: 0.5553 Acc: 0.7059\n",
      "has spend time 102m 1s/n\n",
      "\n",
      "Epoch 2871/9999\n",
      "----------\n",
      "train Loss: 0.4921 Acc: 0.7541\n",
      "has spend time 102m 2s/n\n",
      "val Loss: 0.5602 Acc: 0.6993\n",
      "has spend time 102m 3s/n\n",
      "\n",
      "Epoch 2872/9999\n",
      "----------\n",
      "train Loss: 0.5161 Acc: 0.7172\n",
      "has spend time 102m 5s/n\n",
      "val Loss: 0.5443 Acc: 0.7124\n",
      "has spend time 102m 5s/n\n",
      "\n",
      "Epoch 2873/9999\n",
      "----------\n",
      "train Loss: 0.4922 Acc: 0.7582\n",
      "has spend time 102m 7s/n\n",
      "val Loss: 0.5441 Acc: 0.7190\n",
      "has spend time 102m 7s/n\n",
      "\n",
      "Epoch 2874/9999\n",
      "----------\n",
      "train Loss: 0.5177 Acc: 0.7254\n",
      "has spend time 102m 9s/n\n",
      "val Loss: 0.5480 Acc: 0.6993\n",
      "has spend time 102m 9s/n\n",
      "\n",
      "Epoch 2875/9999\n",
      "----------\n",
      "train Loss: 0.5064 Acc: 0.7459\n",
      "has spend time 102m 11s/n\n",
      "val Loss: 0.5495 Acc: 0.7124\n",
      "has spend time 102m 11s/n\n",
      "\n",
      "Epoch 2876/9999\n",
      "----------\n",
      "train Loss: 0.5191 Acc: 0.7131\n",
      "has spend time 102m 13s/n\n",
      "val Loss: 0.5515 Acc: 0.7059\n",
      "has spend time 102m 13s/n\n",
      "\n",
      "Epoch 2877/9999\n",
      "----------\n",
      "train Loss: 0.5256 Acc: 0.7295\n",
      "has spend time 102m 15s/n\n",
      "val Loss: 0.5527 Acc: 0.6993\n",
      "has spend time 102m 15s/n\n",
      "\n",
      "Epoch 2878/9999\n",
      "----------\n",
      "train Loss: 0.5033 Acc: 0.7295\n",
      "has spend time 102m 17s/n\n",
      "val Loss: 0.5541 Acc: 0.6863\n",
      "has spend time 102m 17s/n\n",
      "\n",
      "Epoch 2879/9999\n",
      "----------\n",
      "train Loss: 0.5014 Acc: 0.7377\n",
      "has spend time 102m 19s/n\n",
      "val Loss: 0.5487 Acc: 0.6928\n",
      "has spend time 102m 19s/n\n",
      "\n",
      "Epoch 2880/9999\n",
      "----------\n",
      "train Loss: 0.5106 Acc: 0.7213\n",
      "has spend time 102m 21s/n\n",
      "val Loss: 0.5440 Acc: 0.7059\n",
      "has spend time 102m 22s/n\n",
      "\n",
      "Epoch 2881/9999\n",
      "----------\n",
      "train Loss: 0.5101 Acc: 0.7582\n",
      "has spend time 102m 23s/n\n",
      "val Loss: 0.5525 Acc: 0.7124\n",
      "has spend time 102m 24s/n\n",
      "\n",
      "Epoch 2882/9999\n",
      "----------\n",
      "train Loss: 0.5154 Acc: 0.7295\n",
      "has spend time 102m 25s/n\n",
      "val Loss: 0.5508 Acc: 0.7190\n",
      "has spend time 102m 26s/n\n",
      "\n",
      "Epoch 2883/9999\n",
      "----------\n",
      "train Loss: 0.4881 Acc: 0.7459\n",
      "has spend time 102m 27s/n\n",
      "val Loss: 0.5446 Acc: 0.7059\n",
      "has spend time 102m 28s/n\n",
      "\n",
      "Epoch 2884/9999\n",
      "----------\n",
      "train Loss: 0.5465 Acc: 0.7049\n",
      "has spend time 102m 29s/n\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val Loss: 0.5586 Acc: 0.7059\n",
      "has spend time 102m 30s/n\n",
      "\n",
      "Epoch 2885/9999\n",
      "----------\n",
      "train Loss: 0.5157 Acc: 0.7500\n",
      "has spend time 102m 31s/n\n",
      "val Loss: 0.5434 Acc: 0.7124\n",
      "has spend time 102m 32s/n\n",
      "\n",
      "Epoch 2886/9999\n",
      "----------\n",
      "train Loss: 0.5375 Acc: 0.7049\n",
      "has spend time 102m 34s/n\n",
      "val Loss: 0.5508 Acc: 0.7059\n",
      "has spend time 102m 34s/n\n",
      "\n",
      "Epoch 2887/9999\n",
      "----------\n",
      "train Loss: 0.5450 Acc: 0.6844\n",
      "has spend time 102m 36s/n\n",
      "val Loss: 0.5456 Acc: 0.7190\n",
      "has spend time 102m 37s/n\n",
      "\n",
      "Epoch 2888/9999\n",
      "----------\n",
      "train Loss: 0.4981 Acc: 0.7418\n",
      "has spend time 102m 38s/n\n",
      "val Loss: 0.5438 Acc: 0.7124\n",
      "has spend time 102m 39s/n\n",
      "\n",
      "Epoch 2889/9999\n",
      "----------\n",
      "train Loss: 0.5126 Acc: 0.7418\n",
      "has spend time 102m 40s/n\n",
      "val Loss: 0.5643 Acc: 0.6928\n",
      "has spend time 102m 41s/n\n",
      "\n",
      "Epoch 2890/9999\n",
      "----------\n",
      "train Loss: 0.5126 Acc: 0.7582\n",
      "has spend time 102m 42s/n\n",
      "val Loss: 0.5505 Acc: 0.7124\n",
      "has spend time 102m 43s/n\n",
      "\n",
      "Epoch 2891/9999\n",
      "----------\n",
      "train Loss: 0.5137 Acc: 0.7377\n",
      "has spend time 102m 44s/n\n",
      "val Loss: 0.5422 Acc: 0.7124\n",
      "has spend time 102m 45s/n\n",
      "\n",
      "Epoch 2892/9999\n",
      "----------\n",
      "train Loss: 0.5314 Acc: 0.7295\n",
      "has spend time 102m 47s/n\n",
      "val Loss: 0.5477 Acc: 0.7059\n",
      "has spend time 102m 47s/n\n",
      "\n",
      "Epoch 2893/9999\n",
      "----------\n",
      "train Loss: 0.5176 Acc: 0.7623\n",
      "has spend time 102m 49s/n\n",
      "val Loss: 0.5379 Acc: 0.7124\n",
      "has spend time 102m 49s/n\n",
      "\n",
      "Epoch 2894/9999\n",
      "----------\n",
      "train Loss: 0.4854 Acc: 0.7705\n",
      "has spend time 102m 51s/n\n",
      "val Loss: 0.5601 Acc: 0.6993\n",
      "has spend time 102m 51s/n\n",
      "\n",
      "Epoch 2895/9999\n",
      "----------\n",
      "train Loss: 0.5180 Acc: 0.7295\n",
      "has spend time 102m 53s/n\n",
      "val Loss: 0.5569 Acc: 0.6993\n",
      "has spend time 102m 54s/n\n",
      "\n",
      "Epoch 2896/9999\n",
      "----------\n",
      "train Loss: 0.5295 Acc: 0.7131\n",
      "has spend time 102m 55s/n\n",
      "val Loss: 0.5711 Acc: 0.6928\n",
      "has spend time 102m 56s/n\n",
      "\n",
      "Epoch 2897/9999\n",
      "----------\n",
      "train Loss: 0.5075 Acc: 0.7541\n",
      "has spend time 102m 57s/n\n",
      "val Loss: 0.5539 Acc: 0.7059\n",
      "has spend time 102m 58s/n\n",
      "\n",
      "Epoch 2898/9999\n",
      "----------\n",
      "train Loss: 0.5139 Acc: 0.7008\n",
      "has spend time 102m 59s/n\n",
      "val Loss: 0.5531 Acc: 0.7059\n",
      "has spend time 102m 60s/n\n",
      "\n",
      "Epoch 2899/9999\n",
      "----------\n",
      "train Loss: 0.5330 Acc: 0.7172\n",
      "has spend time 103m 1s/n\n",
      "val Loss: 0.5532 Acc: 0.6993\n",
      "has spend time 103m 2s/n\n",
      "\n",
      "Epoch 2900/9999\n",
      "----------\n",
      "train Loss: 0.5020 Acc: 0.7418\n",
      "has spend time 103m 3s/n\n",
      "val Loss: 0.5401 Acc: 0.7190\n",
      "has spend time 103m 4s/n\n",
      "\n",
      "Epoch 2901/9999\n",
      "----------\n",
      "train Loss: 0.4932 Acc: 0.7705\n",
      "has spend time 103m 6s/n\n",
      "val Loss: 0.5386 Acc: 0.7255\n",
      "has spend time 103m 6s/n\n",
      "\n",
      "Epoch 2902/9999\n",
      "----------\n",
      "train Loss: 0.4868 Acc: 0.7377\n",
      "has spend time 103m 8s/n\n",
      "val Loss: 0.5452 Acc: 0.7124\n",
      "has spend time 103m 8s/n\n",
      "\n",
      "Epoch 2903/9999\n",
      "----------\n",
      "train Loss: 0.4903 Acc: 0.7295\n",
      "has spend time 103m 10s/n\n",
      "val Loss: 0.5574 Acc: 0.6993\n",
      "has spend time 103m 10s/n\n",
      "\n",
      "Epoch 2904/9999\n",
      "----------\n",
      "train Loss: 0.5041 Acc: 0.7418\n",
      "has spend time 103m 12s/n\n",
      "val Loss: 0.5734 Acc: 0.6993\n",
      "has spend time 103m 12s/n\n",
      "\n",
      "Epoch 2905/9999\n",
      "----------\n",
      "train Loss: 0.5080 Acc: 0.7213\n",
      "has spend time 103m 14s/n\n",
      "val Loss: 0.5519 Acc: 0.7190\n",
      "has spend time 103m 14s/n\n",
      "\n",
      "Epoch 2906/9999\n",
      "----------\n",
      "train Loss: 0.5079 Acc: 0.7418\n",
      "has spend time 103m 16s/n\n",
      "val Loss: 0.5564 Acc: 0.6928\n",
      "has spend time 103m 16s/n\n",
      "\n",
      "Epoch 2907/9999\n",
      "----------\n",
      "train Loss: 0.4932 Acc: 0.7418\n",
      "has spend time 103m 18s/n\n",
      "val Loss: 0.5498 Acc: 0.7255\n",
      "has spend time 103m 19s/n\n",
      "\n",
      "Epoch 2908/9999\n",
      "----------\n",
      "train Loss: 0.5054 Acc: 0.7213\n",
      "has spend time 103m 20s/n\n",
      "val Loss: 0.5515 Acc: 0.7190\n",
      "has spend time 103m 21s/n\n",
      "\n",
      "Epoch 2909/9999\n",
      "----------\n",
      "train Loss: 0.5306 Acc: 0.7090\n",
      "has spend time 103m 22s/n\n",
      "val Loss: 0.5609 Acc: 0.6928\n",
      "has spend time 103m 23s/n\n",
      "\n",
      "Epoch 2910/9999\n",
      "----------\n",
      "train Loss: 0.4892 Acc: 0.7131\n",
      "has spend time 103m 24s/n\n",
      "val Loss: 0.5525 Acc: 0.7059\n",
      "has spend time 103m 25s/n\n",
      "\n",
      "Epoch 2911/9999\n",
      "----------\n",
      "train Loss: 0.4850 Acc: 0.7418\n",
      "has spend time 103m 27s/n\n",
      "val Loss: 0.5429 Acc: 0.7190\n",
      "has spend time 103m 27s/n\n",
      "\n",
      "Epoch 2912/9999\n",
      "----------\n",
      "train Loss: 0.5096 Acc: 0.7500\n",
      "has spend time 103m 29s/n\n",
      "val Loss: 0.5457 Acc: 0.7190\n",
      "has spend time 103m 29s/n\n",
      "\n",
      "Epoch 2913/9999\n",
      "----------\n",
      "train Loss: 0.5011 Acc: 0.7746\n",
      "has spend time 103m 31s/n\n",
      "val Loss: 0.5443 Acc: 0.7124\n",
      "has spend time 103m 31s/n\n",
      "\n",
      "Epoch 2914/9999\n",
      "----------\n",
      "train Loss: 0.4990 Acc: 0.7828\n",
      "has spend time 103m 33s/n\n",
      "val Loss: 0.5513 Acc: 0.7059\n",
      "has spend time 103m 33s/n\n",
      "\n",
      "Epoch 2915/9999\n",
      "----------\n",
      "train Loss: 0.4823 Acc: 0.7664\n",
      "has spend time 103m 35s/n\n",
      "val Loss: 0.5521 Acc: 0.7124\n",
      "has spend time 103m 36s/n\n",
      "\n",
      "Epoch 2916/9999\n",
      "----------\n",
      "train Loss: 0.4861 Acc: 0.7418\n",
      "has spend time 103m 37s/n\n",
      "val Loss: 0.5479 Acc: 0.7124\n",
      "has spend time 103m 38s/n\n",
      "\n",
      "Epoch 2917/9999\n",
      "----------\n",
      "train Loss: 0.5248 Acc: 0.7213\n",
      "has spend time 103m 39s/n\n",
      "val Loss: 0.5522 Acc: 0.7190\n",
      "has spend time 103m 40s/n\n",
      "\n",
      "Epoch 2918/9999\n",
      "----------\n",
      "train Loss: 0.5462 Acc: 0.7172\n",
      "has spend time 103m 41s/n\n",
      "val Loss: 0.5416 Acc: 0.7190\n",
      "has spend time 103m 42s/n\n",
      "\n",
      "Epoch 2919/9999\n",
      "----------\n",
      "train Loss: 0.4786 Acc: 0.7705\n",
      "has spend time 103m 43s/n\n",
      "val Loss: 0.5463 Acc: 0.7124\n",
      "has spend time 103m 44s/n\n",
      "\n",
      "Epoch 2920/9999\n",
      "----------\n",
      "train Loss: 0.5009 Acc: 0.7336\n",
      "has spend time 103m 45s/n\n",
      "val Loss: 0.5542 Acc: 0.7190\n",
      "has spend time 103m 46s/n\n",
      "\n",
      "Epoch 2921/9999\n",
      "----------\n",
      "train Loss: 0.5104 Acc: 0.7541\n",
      "has spend time 103m 47s/n\n",
      "val Loss: 0.5437 Acc: 0.7190\n",
      "has spend time 103m 48s/n\n",
      "\n",
      "Epoch 2922/9999\n",
      "----------\n",
      "train Loss: 0.5098 Acc: 0.7623\n",
      "has spend time 103m 49s/n\n",
      "val Loss: 0.5476 Acc: 0.7059\n",
      "has spend time 103m 50s/n\n",
      "\n",
      "Epoch 2923/9999\n",
      "----------\n",
      "train Loss: 0.4954 Acc: 0.7746\n",
      "has spend time 103m 52s/n\n",
      "val Loss: 0.5446 Acc: 0.7124\n",
      "has spend time 103m 53s/n\n",
      "\n",
      "Epoch 2924/9999\n",
      "----------\n",
      "train Loss: 0.5191 Acc: 0.7336\n",
      "has spend time 103m 54s/n\n",
      "val Loss: 0.5455 Acc: 0.7059\n",
      "has spend time 103m 55s/n\n",
      "\n",
      "Epoch 2925/9999\n",
      "----------\n",
      "train Loss: 0.4944 Acc: 0.7582\n",
      "has spend time 103m 57s/n\n",
      "val Loss: 0.5637 Acc: 0.6993\n",
      "has spend time 103m 57s/n\n",
      "\n",
      "Epoch 2926/9999\n",
      "----------\n",
      "train Loss: 0.5068 Acc: 0.7131\n",
      "has spend time 103m 59s/n\n",
      "val Loss: 0.5576 Acc: 0.7059\n",
      "has spend time 103m 59s/n\n",
      "\n",
      "Epoch 2927/9999\n",
      "----------\n",
      "train Loss: 0.4837 Acc: 0.7418\n",
      "has spend time 104m 1s/n\n",
      "val Loss: 0.5438 Acc: 0.7059\n",
      "has spend time 104m 1s/n\n",
      "\n",
      "Epoch 2928/9999\n",
      "----------\n",
      "train Loss: 0.5162 Acc: 0.7377\n",
      "has spend time 104m 3s/n\n",
      "val Loss: 0.5475 Acc: 0.7059\n",
      "has spend time 104m 3s/n\n",
      "\n",
      "Epoch 2929/9999\n",
      "----------\n",
      "train Loss: 0.4677 Acc: 0.7705\n",
      "has spend time 104m 5s/n\n",
      "val Loss: 0.5567 Acc: 0.7059\n",
      "has spend time 104m 6s/n\n",
      "\n",
      "Epoch 2930/9999\n",
      "----------\n",
      "train Loss: 0.4958 Acc: 0.7705\n",
      "has spend time 104m 7s/n\n",
      "val Loss: 0.5451 Acc: 0.7124\n",
      "has spend time 104m 8s/n\n",
      "\n",
      "Epoch 2931/9999\n",
      "----------\n",
      "train Loss: 0.5172 Acc: 0.7500\n",
      "has spend time 104m 9s/n\n",
      "val Loss: 0.5498 Acc: 0.7059\n",
      "has spend time 104m 10s/n\n",
      "\n",
      "Epoch 2932/9999\n",
      "----------\n",
      "train Loss: 0.5284 Acc: 0.7008\n",
      "has spend time 104m 11s/n\n",
      "val Loss: 0.5687 Acc: 0.6863\n",
      "has spend time 104m 12s/n\n",
      "\n",
      "Epoch 2933/9999\n",
      "----------\n",
      "train Loss: 0.4838 Acc: 0.7500\n",
      "has spend time 104m 13s/n\n",
      "val Loss: 0.5504 Acc: 0.6993\n",
      "has spend time 104m 14s/n\n",
      "\n",
      "Epoch 2934/9999\n",
      "----------\n",
      "train Loss: 0.5062 Acc: 0.7377\n",
      "has spend time 104m 15s/n\n",
      "val Loss: 0.5415 Acc: 0.7255\n",
      "has spend time 104m 16s/n\n",
      "\n",
      "Epoch 2935/9999\n",
      "----------\n",
      "train Loss: 0.4849 Acc: 0.7500\n",
      "has spend time 104m 18s/n\n",
      "val Loss: 0.5495 Acc: 0.7059\n",
      "has spend time 104m 18s/n\n",
      "\n",
      "Epoch 2936/9999\n",
      "----------\n",
      "train Loss: 0.5025 Acc: 0.7295\n",
      "has spend time 104m 20s/n\n",
      "val Loss: 0.5449 Acc: 0.7059\n",
      "has spend time 104m 21s/n\n",
      "\n",
      "Epoch 2937/9999\n",
      "----------\n",
      "train Loss: 0.5081 Acc: 0.7213\n",
      "has spend time 104m 22s/n\n",
      "val Loss: 0.5467 Acc: 0.7059\n",
      "has spend time 104m 23s/n\n",
      "\n",
      "Epoch 2938/9999\n",
      "----------\n",
      "train Loss: 0.4931 Acc: 0.7418\n",
      "has spend time 104m 24s/n\n",
      "val Loss: 0.5522 Acc: 0.7124\n",
      "has spend time 104m 25s/n\n",
      "\n",
      "Epoch 2939/9999\n",
      "----------\n",
      "train Loss: 0.5184 Acc: 0.7254\n",
      "has spend time 104m 26s/n\n",
      "val Loss: 0.5561 Acc: 0.7059\n",
      "has spend time 104m 27s/n\n",
      "\n",
      "Epoch 2940/9999\n",
      "----------\n",
      "train Loss: 0.4924 Acc: 0.7254\n",
      "has spend time 104m 28s/n\n",
      "val Loss: 0.5577 Acc: 0.6993\n",
      "has spend time 104m 29s/n\n",
      "\n",
      "Epoch 2941/9999\n",
      "----------\n",
      "train Loss: 0.5081 Acc: 0.7500\n",
      "has spend time 104m 30s/n\n",
      "val Loss: 0.5539 Acc: 0.7124\n",
      "has spend time 104m 31s/n\n",
      "\n",
      "Epoch 2942/9999\n",
      "----------\n",
      "train Loss: 0.5352 Acc: 0.6885\n",
      "has spend time 104m 33s/n\n",
      "val Loss: 0.5523 Acc: 0.7124\n",
      "has spend time 104m 33s/n\n",
      "\n",
      "Epoch 2943/9999\n",
      "----------\n",
      "train Loss: 0.4926 Acc: 0.7541\n",
      "has spend time 104m 35s/n\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val Loss: 0.5482 Acc: 0.7059\n",
      "has spend time 104m 35s/n\n",
      "\n",
      "Epoch 2944/9999\n",
      "----------\n",
      "train Loss: 0.5305 Acc: 0.7131\n",
      "has spend time 104m 37s/n\n",
      "val Loss: 0.5446 Acc: 0.7059\n",
      "has spend time 104m 37s/n\n",
      "\n",
      "Epoch 2945/9999\n",
      "----------\n",
      "train Loss: 0.4832 Acc: 0.7418\n",
      "has spend time 104m 39s/n\n",
      "val Loss: 0.5384 Acc: 0.7190\n",
      "has spend time 104m 39s/n\n",
      "\n",
      "Epoch 2946/9999\n",
      "----------\n",
      "train Loss: 0.5327 Acc: 0.7254\n",
      "has spend time 104m 41s/n\n",
      "val Loss: 0.5559 Acc: 0.7124\n",
      "has spend time 104m 41s/n\n",
      "\n",
      "Epoch 2947/9999\n",
      "----------\n",
      "train Loss: 0.4961 Acc: 0.7418\n",
      "has spend time 104m 43s/n\n",
      "val Loss: 0.5473 Acc: 0.7190\n",
      "has spend time 104m 43s/n\n",
      "\n",
      "Epoch 2948/9999\n",
      "----------\n",
      "train Loss: 0.4910 Acc: 0.7541\n",
      "has spend time 104m 45s/n\n",
      "val Loss: 0.5470 Acc: 0.7124\n",
      "has spend time 104m 46s/n\n",
      "\n",
      "Epoch 2949/9999\n",
      "----------\n",
      "train Loss: 0.4639 Acc: 0.7664\n",
      "has spend time 104m 47s/n\n",
      "val Loss: 0.5447 Acc: 0.7124\n",
      "has spend time 104m 48s/n\n",
      "\n",
      "Epoch 2950/9999\n",
      "----------\n",
      "train Loss: 0.5143 Acc: 0.7336\n",
      "has spend time 104m 49s/n\n",
      "val Loss: 0.5576 Acc: 0.6993\n",
      "has spend time 104m 50s/n\n",
      "\n",
      "Epoch 2951/9999\n",
      "----------\n",
      "train Loss: 0.5231 Acc: 0.7254\n",
      "has spend time 104m 51s/n\n",
      "val Loss: 0.5410 Acc: 0.7190\n",
      "has spend time 104m 52s/n\n",
      "\n",
      "Epoch 2952/9999\n",
      "----------\n",
      "train Loss: 0.4862 Acc: 0.7500\n",
      "has spend time 104m 54s/n\n",
      "val Loss: 0.5515 Acc: 0.7059\n",
      "has spend time 104m 54s/n\n",
      "\n",
      "Epoch 2953/9999\n",
      "----------\n",
      "train Loss: 0.4986 Acc: 0.7295\n",
      "has spend time 104m 56s/n\n",
      "val Loss: 0.5549 Acc: 0.7059\n",
      "has spend time 104m 56s/n\n",
      "\n",
      "Epoch 2954/9999\n",
      "----------\n",
      "train Loss: 0.4914 Acc: 0.7418\n",
      "has spend time 104m 58s/n\n",
      "val Loss: 0.5684 Acc: 0.6863\n",
      "has spend time 104m 58s/n\n",
      "\n",
      "Epoch 2955/9999\n",
      "----------\n",
      "train Loss: 0.4954 Acc: 0.7582\n",
      "has spend time 104m 60s/n\n",
      "val Loss: 0.5454 Acc: 0.7190\n",
      "has spend time 105m 0s/n\n",
      "\n",
      "Epoch 2956/9999\n",
      "----------\n",
      "train Loss: 0.4980 Acc: 0.7336\n",
      "has spend time 105m 2s/n\n",
      "val Loss: 0.5553 Acc: 0.6993\n",
      "has spend time 105m 3s/n\n",
      "\n",
      "Epoch 2957/9999\n",
      "----------\n",
      "train Loss: 0.5085 Acc: 0.7418\n",
      "has spend time 105m 4s/n\n",
      "val Loss: 0.5506 Acc: 0.7124\n",
      "has spend time 105m 5s/n\n",
      "\n",
      "Epoch 2958/9999\n",
      "----------\n",
      "train Loss: 0.5152 Acc: 0.7295\n",
      "has spend time 105m 7s/n\n",
      "val Loss: 0.5485 Acc: 0.7124\n",
      "has spend time 105m 7s/n\n",
      "\n",
      "Epoch 2959/9999\n",
      "----------\n",
      "train Loss: 0.5308 Acc: 0.7418\n",
      "has spend time 105m 9s/n\n",
      "val Loss: 0.5495 Acc: 0.7124\n",
      "has spend time 105m 9s/n\n",
      "\n",
      "Epoch 2960/9999\n",
      "----------\n",
      "train Loss: 0.5181 Acc: 0.7418\n",
      "has spend time 105m 11s/n\n",
      "val Loss: 0.5513 Acc: 0.7059\n",
      "has spend time 105m 11s/n\n",
      "\n",
      "Epoch 2961/9999\n",
      "----------\n",
      "train Loss: 0.4758 Acc: 0.7623\n",
      "has spend time 105m 13s/n\n",
      "val Loss: 0.5530 Acc: 0.7059\n",
      "has spend time 105m 13s/n\n",
      "\n",
      "Epoch 2962/9999\n",
      "----------\n",
      "train Loss: 0.5135 Acc: 0.7418\n",
      "has spend time 105m 15s/n\n",
      "val Loss: 0.5522 Acc: 0.7124\n",
      "has spend time 105m 15s/n\n",
      "\n",
      "Epoch 2963/9999\n",
      "----------\n",
      "train Loss: 0.5153 Acc: 0.7336\n",
      "has spend time 105m 17s/n\n",
      "val Loss: 0.5673 Acc: 0.6928\n",
      "has spend time 105m 17s/n\n",
      "\n",
      "Epoch 2964/9999\n",
      "----------\n",
      "train Loss: 0.4985 Acc: 0.7459\n",
      "has spend time 105m 19s/n\n",
      "val Loss: 0.5455 Acc: 0.7386\n",
      "has spend time 105m 19s/n\n",
      "\n",
      "Epoch 2965/9999\n",
      "----------\n",
      "train Loss: 0.5378 Acc: 0.7664\n",
      "has spend time 105m 21s/n\n",
      "val Loss: 0.5417 Acc: 0.7255\n",
      "has spend time 105m 21s/n\n",
      "\n",
      "Epoch 2966/9999\n",
      "----------\n",
      "train Loss: 0.5327 Acc: 0.7172\n",
      "has spend time 105m 23s/n\n",
      "val Loss: 0.5420 Acc: 0.7059\n",
      "has spend time 105m 23s/n\n",
      "\n",
      "Epoch 2967/9999\n",
      "----------\n",
      "train Loss: 0.5148 Acc: 0.7377\n",
      "has spend time 105m 25s/n\n",
      "val Loss: 0.5402 Acc: 0.7255\n",
      "has spend time 105m 26s/n\n",
      "\n",
      "Epoch 2968/9999\n",
      "----------\n",
      "train Loss: 0.5332 Acc: 0.7131\n",
      "has spend time 105m 27s/n\n",
      "val Loss: 0.5380 Acc: 0.7124\n",
      "has spend time 105m 28s/n\n",
      "\n",
      "Epoch 2969/9999\n",
      "----------\n",
      "train Loss: 0.5184 Acc: 0.7418\n",
      "has spend time 105m 29s/n\n",
      "val Loss: 0.5534 Acc: 0.7059\n",
      "has spend time 105m 30s/n\n",
      "\n",
      "Epoch 2970/9999\n",
      "----------\n",
      "train Loss: 0.4827 Acc: 0.7623\n",
      "has spend time 105m 31s/n\n",
      "val Loss: 0.5630 Acc: 0.6993\n",
      "has spend time 105m 32s/n\n",
      "\n",
      "Epoch 2971/9999\n",
      "----------\n",
      "train Loss: 0.5023 Acc: 0.7500\n",
      "has spend time 105m 34s/n\n",
      "val Loss: 0.5644 Acc: 0.6993\n",
      "has spend time 105m 34s/n\n",
      "\n",
      "Epoch 2972/9999\n",
      "----------\n",
      "train Loss: 0.5267 Acc: 0.7377\n",
      "has spend time 105m 36s/n\n",
      "val Loss: 0.5439 Acc: 0.7059\n",
      "has spend time 105m 37s/n\n",
      "\n",
      "Epoch 2973/9999\n",
      "----------\n",
      "train Loss: 0.5008 Acc: 0.7377\n",
      "has spend time 105m 38s/n\n",
      "val Loss: 0.5543 Acc: 0.7124\n",
      "has spend time 105m 39s/n\n",
      "\n",
      "Epoch 2974/9999\n",
      "----------\n",
      "train Loss: 0.5066 Acc: 0.7377\n",
      "has spend time 105m 40s/n\n",
      "val Loss: 0.5502 Acc: 0.7059\n",
      "has spend time 105m 41s/n\n",
      "\n",
      "Epoch 2975/9999\n",
      "----------\n",
      "train Loss: 0.5055 Acc: 0.7295\n",
      "has spend time 105m 43s/n\n",
      "val Loss: 0.5454 Acc: 0.7190\n",
      "has spend time 105m 43s/n\n",
      "\n",
      "Epoch 2976/9999\n",
      "----------\n",
      "train Loss: 0.5323 Acc: 0.7090\n",
      "has spend time 105m 45s/n\n",
      "val Loss: 0.5459 Acc: 0.7255\n",
      "has spend time 105m 45s/n\n",
      "\n",
      "Epoch 2977/9999\n",
      "----------\n",
      "train Loss: 0.4950 Acc: 0.7254\n",
      "has spend time 105m 47s/n\n",
      "val Loss: 0.5472 Acc: 0.7059\n",
      "has spend time 105m 47s/n\n",
      "\n",
      "Epoch 2978/9999\n",
      "----------\n",
      "train Loss: 0.5097 Acc: 0.7131\n",
      "has spend time 105m 49s/n\n",
      "val Loss: 0.5411 Acc: 0.7124\n",
      "has spend time 105m 49s/n\n",
      "\n",
      "Epoch 2979/9999\n",
      "----------\n",
      "train Loss: 0.4852 Acc: 0.7459\n",
      "has spend time 105m 51s/n\n",
      "val Loss: 0.5466 Acc: 0.7059\n",
      "has spend time 105m 52s/n\n",
      "\n",
      "Epoch 2980/9999\n",
      "----------\n",
      "train Loss: 0.5186 Acc: 0.7254\n",
      "has spend time 105m 53s/n\n",
      "val Loss: 0.5492 Acc: 0.7124\n",
      "has spend time 105m 54s/n\n",
      "\n",
      "Epoch 2981/9999\n",
      "----------\n",
      "train Loss: 0.5414 Acc: 0.7295\n",
      "has spend time 105m 55s/n\n",
      "val Loss: 0.5505 Acc: 0.7190\n",
      "has spend time 105m 56s/n\n",
      "\n",
      "Epoch 2982/9999\n",
      "----------\n",
      "train Loss: 0.5034 Acc: 0.7295\n",
      "has spend time 105m 57s/n\n",
      "val Loss: 0.5568 Acc: 0.6993\n",
      "has spend time 105m 58s/n\n",
      "\n",
      "Epoch 2983/9999\n",
      "----------\n",
      "train Loss: 0.4999 Acc: 0.7500\n",
      "has spend time 105m 59s/n\n",
      "val Loss: 0.5446 Acc: 0.7190\n",
      "has spend time 105m 60s/n\n",
      "\n",
      "Epoch 2984/9999\n",
      "----------\n",
      "train Loss: 0.5011 Acc: 0.7500\n",
      "has spend time 106m 1s/n\n",
      "val Loss: 0.5537 Acc: 0.7059\n",
      "has spend time 106m 2s/n\n",
      "\n",
      "Epoch 2985/9999\n",
      "----------\n",
      "train Loss: 0.4839 Acc: 0.7869\n",
      "has spend time 106m 3s/n\n",
      "val Loss: 0.5573 Acc: 0.7124\n",
      "has spend time 106m 4s/n\n",
      "\n",
      "Epoch 2986/9999\n",
      "----------\n",
      "train Loss: 0.5043 Acc: 0.7582\n",
      "has spend time 106m 5s/n\n",
      "val Loss: 0.5474 Acc: 0.7190\n",
      "has spend time 106m 6s/n\n",
      "\n",
      "Epoch 2987/9999\n",
      "----------\n",
      "train Loss: 0.5025 Acc: 0.7582\n",
      "has spend time 106m 8s/n\n",
      "val Loss: 0.5468 Acc: 0.7124\n",
      "has spend time 106m 9s/n\n",
      "\n",
      "Epoch 2988/9999\n",
      "----------\n",
      "train Loss: 0.5245 Acc: 0.7254\n",
      "has spend time 106m 10s/n\n",
      "val Loss: 0.5658 Acc: 0.7059\n",
      "has spend time 106m 11s/n\n",
      "\n",
      "Epoch 2989/9999\n",
      "----------\n",
      "train Loss: 0.4871 Acc: 0.7705\n",
      "has spend time 106m 12s/n\n",
      "val Loss: 0.5550 Acc: 0.7124\n",
      "has spend time 106m 13s/n\n",
      "\n",
      "Epoch 2990/9999\n",
      "----------\n",
      "train Loss: 0.5020 Acc: 0.7377\n",
      "has spend time 106m 14s/n\n",
      "val Loss: 0.5507 Acc: 0.6993\n",
      "has spend time 106m 15s/n\n",
      "\n",
      "Epoch 2991/9999\n",
      "----------\n",
      "train Loss: 0.5009 Acc: 0.7377\n",
      "has spend time 106m 16s/n\n",
      "val Loss: 0.5507 Acc: 0.7190\n",
      "has spend time 106m 17s/n\n",
      "\n",
      "Epoch 2992/9999\n",
      "----------\n",
      "train Loss: 0.4834 Acc: 0.7418\n",
      "has spend time 106m 18s/n\n",
      "val Loss: 0.5474 Acc: 0.7190\n",
      "has spend time 106m 19s/n\n",
      "\n",
      "Epoch 2993/9999\n",
      "----------\n",
      "train Loss: 0.5379 Acc: 0.7172\n",
      "has spend time 106m 21s/n\n",
      "val Loss: 0.5565 Acc: 0.7059\n",
      "has spend time 106m 21s/n\n",
      "\n",
      "Epoch 2994/9999\n",
      "----------\n",
      "train Loss: 0.5226 Acc: 0.7254\n",
      "has spend time 106m 23s/n\n",
      "val Loss: 0.5586 Acc: 0.7059\n",
      "has spend time 106m 23s/n\n",
      "\n",
      "Epoch 2995/9999\n",
      "----------\n",
      "train Loss: 0.4937 Acc: 0.7459\n",
      "has spend time 106m 25s/n\n",
      "val Loss: 0.5403 Acc: 0.7190\n",
      "has spend time 106m 25s/n\n",
      "\n",
      "Epoch 2996/9999\n",
      "----------\n",
      "train Loss: 0.5213 Acc: 0.7172\n",
      "has spend time 106m 27s/n\n",
      "val Loss: 0.5603 Acc: 0.6993\n",
      "has spend time 106m 27s/n\n",
      "\n",
      "Epoch 2997/9999\n",
      "----------\n",
      "train Loss: 0.5061 Acc: 0.7254\n",
      "has spend time 106m 29s/n\n",
      "val Loss: 0.5513 Acc: 0.6993\n",
      "has spend time 106m 29s/n\n",
      "\n",
      "Epoch 2998/9999\n",
      "----------\n",
      "train Loss: 0.5423 Acc: 0.7131\n",
      "has spend time 106m 31s/n\n",
      "val Loss: 0.5550 Acc: 0.7124\n",
      "has spend time 106m 31s/n\n",
      "\n",
      "Epoch 2999/9999\n",
      "----------\n",
      "train Loss: 0.4964 Acc: 0.7500\n",
      "has spend time 106m 33s/n\n",
      "val Loss: 0.5450 Acc: 0.7124\n",
      "has spend time 106m 33s/n\n",
      "\n",
      "Epoch 3000/9999\n",
      "----------\n",
      "train Loss: 0.5062 Acc: 0.7172\n",
      "has spend time 106m 35s/n\n",
      "val Loss: 0.5521 Acc: 0.7124\n",
      "has spend time 106m 35s/n\n",
      "\n",
      "Epoch 3001/9999\n",
      "----------\n",
      "train Loss: 0.4996 Acc: 0.7172\n",
      "has spend time 106m 37s/n\n",
      "val Loss: 0.5515 Acc: 0.7124\n",
      "has spend time 106m 37s/n\n",
      "\n",
      "Epoch 3002/9999\n",
      "----------\n",
      "train Loss: 0.5062 Acc: 0.7459\n",
      "has spend time 106m 39s/n\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val Loss: 0.5536 Acc: 0.7059\n",
      "has spend time 106m 40s/n\n",
      "\n",
      "Epoch 3003/9999\n",
      "----------\n",
      "train Loss: 0.4938 Acc: 0.7705\n",
      "has spend time 106m 41s/n\n",
      "val Loss: 0.5481 Acc: 0.7059\n",
      "has spend time 106m 42s/n\n",
      "\n",
      "Epoch 3004/9999\n",
      "----------\n",
      "train Loss: 0.4935 Acc: 0.7623\n",
      "has spend time 106m 43s/n\n",
      "val Loss: 0.5519 Acc: 0.7059\n",
      "has spend time 106m 44s/n\n",
      "\n",
      "Epoch 3005/9999\n",
      "----------\n",
      "train Loss: 0.4988 Acc: 0.7500\n",
      "has spend time 106m 46s/n\n",
      "val Loss: 0.5493 Acc: 0.7059\n",
      "has spend time 106m 47s/n\n",
      "\n",
      "Epoch 3006/9999\n",
      "----------\n",
      "train Loss: 0.4672 Acc: 0.7500\n",
      "has spend time 106m 48s/n\n",
      "val Loss: 0.5587 Acc: 0.7059\n",
      "has spend time 106m 49s/n\n",
      "\n",
      "Epoch 3007/9999\n",
      "----------\n",
      "train Loss: 0.5242 Acc: 0.7500\n",
      "has spend time 106m 50s/n\n",
      "val Loss: 0.5613 Acc: 0.6928\n",
      "has spend time 106m 51s/n\n",
      "\n",
      "Epoch 3008/9999\n",
      "----------\n",
      "train Loss: 0.5087 Acc: 0.7336\n",
      "has spend time 106m 52s/n\n",
      "val Loss: 0.5497 Acc: 0.7059\n",
      "has spend time 106m 53s/n\n",
      "\n",
      "Epoch 3009/9999\n",
      "----------\n",
      "train Loss: 0.5199 Acc: 0.7254\n",
      "has spend time 106m 54s/n\n",
      "val Loss: 0.5491 Acc: 0.7124\n",
      "has spend time 106m 55s/n\n",
      "\n",
      "Epoch 3010/9999\n",
      "----------\n",
      "train Loss: 0.5200 Acc: 0.7213\n",
      "has spend time 106m 56s/n\n",
      "val Loss: 0.5602 Acc: 0.6797\n",
      "has spend time 106m 57s/n\n",
      "\n",
      "Epoch 3011/9999\n",
      "----------\n",
      "train Loss: 0.5205 Acc: 0.7254\n",
      "has spend time 106m 58s/n\n",
      "val Loss: 0.5580 Acc: 0.6797\n",
      "has spend time 106m 59s/n\n",
      "\n",
      "Epoch 3012/9999\n",
      "----------\n",
      "train Loss: 0.5306 Acc: 0.7418\n",
      "has spend time 107m 0s/n\n",
      "val Loss: 0.5496 Acc: 0.7124\n",
      "has spend time 107m 1s/n\n",
      "\n",
      "Epoch 3013/9999\n",
      "----------\n",
      "train Loss: 0.5171 Acc: 0.7418\n",
      "has spend time 107m 2s/n\n",
      "val Loss: 0.5577 Acc: 0.7059\n",
      "has spend time 107m 3s/n\n",
      "\n",
      "Epoch 3014/9999\n",
      "----------\n",
      "train Loss: 0.5050 Acc: 0.7377\n",
      "has spend time 107m 4s/n\n",
      "val Loss: 0.5555 Acc: 0.6928\n",
      "has spend time 107m 5s/n\n",
      "\n",
      "Epoch 3015/9999\n",
      "----------\n",
      "train Loss: 0.4929 Acc: 0.7254\n",
      "has spend time 107m 7s/n\n",
      "val Loss: 0.5496 Acc: 0.6993\n",
      "has spend time 107m 7s/n\n",
      "\n",
      "Epoch 3016/9999\n",
      "----------\n",
      "train Loss: 0.4897 Acc: 0.7459\n",
      "has spend time 107m 9s/n\n",
      "val Loss: 0.5511 Acc: 0.7059\n",
      "has spend time 107m 9s/n\n",
      "\n",
      "Epoch 3017/9999\n",
      "----------\n",
      "train Loss: 0.5355 Acc: 0.7336\n",
      "has spend time 107m 11s/n\n",
      "val Loss: 0.5496 Acc: 0.7059\n",
      "has spend time 107m 12s/n\n",
      "\n",
      "Epoch 3018/9999\n",
      "----------\n",
      "train Loss: 0.5029 Acc: 0.7459\n",
      "has spend time 107m 13s/n\n",
      "val Loss: 0.5492 Acc: 0.7190\n",
      "has spend time 107m 14s/n\n",
      "\n",
      "Epoch 3019/9999\n",
      "----------\n",
      "train Loss: 0.5179 Acc: 0.7213\n",
      "has spend time 107m 15s/n\n",
      "val Loss: 0.5547 Acc: 0.7190\n",
      "has spend time 107m 16s/n\n",
      "\n",
      "Epoch 3020/9999\n",
      "----------\n",
      "train Loss: 0.5006 Acc: 0.7541\n",
      "has spend time 107m 17s/n\n",
      "val Loss: 0.5757 Acc: 0.6863\n",
      "has spend time 107m 18s/n\n",
      "\n",
      "Epoch 3021/9999\n",
      "----------\n",
      "train Loss: 0.5509 Acc: 0.7008\n",
      "has spend time 107m 19s/n\n",
      "val Loss: 0.5525 Acc: 0.7190\n",
      "has spend time 107m 20s/n\n",
      "\n",
      "Epoch 3022/9999\n",
      "----------\n",
      "train Loss: 0.5088 Acc: 0.7377\n",
      "has spend time 107m 21s/n\n",
      "val Loss: 0.5437 Acc: 0.7190\n",
      "has spend time 107m 22s/n\n",
      "\n",
      "Epoch 3023/9999\n",
      "----------\n",
      "train Loss: 0.4945 Acc: 0.7623\n",
      "has spend time 107m 24s/n\n",
      "val Loss: 0.5457 Acc: 0.7059\n",
      "has spend time 107m 25s/n\n",
      "\n",
      "Epoch 3024/9999\n",
      "----------\n",
      "train Loss: 0.4910 Acc: 0.7459\n",
      "has spend time 107m 26s/n\n",
      "val Loss: 0.5476 Acc: 0.7124\n",
      "has spend time 107m 27s/n\n",
      "\n",
      "Epoch 3025/9999\n",
      "----------\n",
      "train Loss: 0.4901 Acc: 0.7541\n",
      "has spend time 107m 28s/n\n",
      "val Loss: 0.5431 Acc: 0.7190\n",
      "has spend time 107m 29s/n\n",
      "\n",
      "Epoch 3026/9999\n",
      "----------\n",
      "train Loss: 0.5103 Acc: 0.7418\n",
      "has spend time 107m 30s/n\n",
      "val Loss: 0.5463 Acc: 0.7255\n",
      "has spend time 107m 31s/n\n",
      "\n",
      "Epoch 3027/9999\n",
      "----------\n",
      "train Loss: 0.4995 Acc: 0.7664\n",
      "has spend time 107m 32s/n\n",
      "val Loss: 0.5575 Acc: 0.7059\n",
      "has spend time 107m 33s/n\n",
      "\n",
      "Epoch 3028/9999\n",
      "----------\n",
      "train Loss: 0.5212 Acc: 0.7418\n",
      "has spend time 107m 35s/n\n",
      "val Loss: 0.5569 Acc: 0.7124\n",
      "has spend time 107m 35s/n\n",
      "\n",
      "Epoch 3029/9999\n",
      "----------\n",
      "train Loss: 0.5299 Acc: 0.7500\n",
      "has spend time 107m 37s/n\n",
      "val Loss: 0.5583 Acc: 0.6993\n",
      "has spend time 107m 38s/n\n",
      "\n",
      "Epoch 3030/9999\n",
      "----------\n",
      "train Loss: 0.4818 Acc: 0.8074\n",
      "has spend time 107m 39s/n\n",
      "val Loss: 0.5410 Acc: 0.7255\n",
      "has spend time 107m 40s/n\n",
      "\n",
      "Epoch 3031/9999\n",
      "----------\n",
      "train Loss: 0.5130 Acc: 0.7172\n",
      "has spend time 107m 41s/n\n",
      "val Loss: 0.5521 Acc: 0.7059\n",
      "has spend time 107m 42s/n\n",
      "\n",
      "Epoch 3032/9999\n",
      "----------\n",
      "train Loss: 0.5098 Acc: 0.7254\n",
      "has spend time 107m 43s/n\n",
      "val Loss: 0.5480 Acc: 0.7124\n",
      "has spend time 107m 44s/n\n",
      "\n",
      "Epoch 3033/9999\n",
      "----------\n",
      "train Loss: 0.4984 Acc: 0.7664\n",
      "has spend time 107m 45s/n\n",
      "val Loss: 0.5487 Acc: 0.7190\n",
      "has spend time 107m 46s/n\n",
      "\n",
      "Epoch 3034/9999\n",
      "----------\n",
      "train Loss: 0.5263 Acc: 0.7254\n",
      "has spend time 107m 47s/n\n",
      "val Loss: 0.5622 Acc: 0.7124\n",
      "has spend time 107m 48s/n\n",
      "\n",
      "Epoch 3035/9999\n",
      "----------\n",
      "train Loss: 0.5286 Acc: 0.7377\n",
      "has spend time 107m 49s/n\n",
      "val Loss: 0.5548 Acc: 0.6928\n",
      "has spend time 107m 50s/n\n",
      "\n",
      "Epoch 3036/9999\n",
      "----------\n",
      "train Loss: 0.5238 Acc: 0.7213\n",
      "has spend time 107m 52s/n\n",
      "val Loss: 0.5379 Acc: 0.7255\n",
      "has spend time 107m 52s/n\n",
      "\n",
      "Epoch 3037/9999\n",
      "----------\n",
      "train Loss: 0.4775 Acc: 0.7500\n",
      "has spend time 107m 54s/n\n",
      "val Loss: 0.5417 Acc: 0.7059\n",
      "has spend time 107m 54s/n\n",
      "\n",
      "Epoch 3038/9999\n",
      "----------\n",
      "train Loss: 0.4992 Acc: 0.7418\n",
      "has spend time 107m 56s/n\n",
      "val Loss: 0.5576 Acc: 0.6928\n",
      "has spend time 107m 56s/n\n",
      "\n",
      "Epoch 3039/9999\n",
      "----------\n",
      "train Loss: 0.5079 Acc: 0.7090\n",
      "has spend time 107m 58s/n\n",
      "val Loss: 0.5652 Acc: 0.6928\n",
      "has spend time 107m 58s/n\n",
      "\n",
      "Epoch 3040/9999\n",
      "----------\n",
      "train Loss: 0.5171 Acc: 0.7049\n",
      "has spend time 107m 60s/n\n",
      "val Loss: 0.5493 Acc: 0.7059\n",
      "has spend time 108m 0s/n\n",
      "\n",
      "Epoch 3041/9999\n",
      "----------\n",
      "train Loss: 0.4924 Acc: 0.7541\n",
      "has spend time 108m 2s/n\n",
      "val Loss: 0.5437 Acc: 0.7190\n",
      "has spend time 108m 2s/n\n",
      "\n",
      "Epoch 3042/9999\n",
      "----------\n",
      "train Loss: 0.5125 Acc: 0.7172\n",
      "has spend time 108m 4s/n\n",
      "val Loss: 0.5492 Acc: 0.7190\n",
      "has spend time 108m 5s/n\n",
      "\n",
      "Epoch 3043/9999\n",
      "----------\n",
      "train Loss: 0.4886 Acc: 0.7500\n",
      "has spend time 108m 7s/n\n",
      "val Loss: 0.5451 Acc: 0.7124\n",
      "has spend time 108m 7s/n\n",
      "\n",
      "Epoch 3044/9999\n",
      "----------\n",
      "train Loss: 0.4969 Acc: 0.7623\n",
      "has spend time 108m 9s/n\n",
      "val Loss: 0.5494 Acc: 0.7124\n",
      "has spend time 108m 9s/n\n",
      "\n",
      "Epoch 3045/9999\n",
      "----------\n",
      "train Loss: 0.5056 Acc: 0.7254\n",
      "has spend time 108m 11s/n\n",
      "val Loss: 0.5448 Acc: 0.7124\n",
      "has spend time 108m 11s/n\n",
      "\n",
      "Epoch 3046/9999\n",
      "----------\n",
      "train Loss: 0.4993 Acc: 0.7418\n",
      "has spend time 108m 13s/n\n",
      "val Loss: 0.5370 Acc: 0.7190\n",
      "has spend time 108m 13s/n\n",
      "\n",
      "Epoch 3047/9999\n",
      "----------\n",
      "train Loss: 0.4898 Acc: 0.7787\n",
      "has spend time 108m 15s/n\n",
      "val Loss: 0.5502 Acc: 0.7059\n",
      "has spend time 108m 16s/n\n",
      "\n",
      "Epoch 3048/9999\n",
      "----------\n",
      "train Loss: 0.5298 Acc: 0.7213\n",
      "has spend time 108m 17s/n\n",
      "val Loss: 0.5505 Acc: 0.7124\n",
      "has spend time 108m 18s/n\n",
      "\n",
      "Epoch 3049/9999\n",
      "----------\n",
      "train Loss: 0.5053 Acc: 0.7377\n",
      "has spend time 108m 19s/n\n",
      "val Loss: 0.5523 Acc: 0.6993\n",
      "has spend time 108m 20s/n\n",
      "\n",
      "Epoch 3050/9999\n",
      "----------\n",
      "train Loss: 0.5161 Acc: 0.7295\n",
      "has spend time 108m 22s/n\n",
      "val Loss: 0.5461 Acc: 0.7190\n",
      "has spend time 108m 22s/n\n",
      "\n",
      "Epoch 3051/9999\n",
      "----------\n",
      "train Loss: 0.5447 Acc: 0.7008\n",
      "has spend time 108m 24s/n\n",
      "val Loss: 0.5514 Acc: 0.7124\n",
      "has spend time 108m 25s/n\n",
      "\n",
      "Epoch 3052/9999\n",
      "----------\n",
      "train Loss: 0.5067 Acc: 0.7336\n",
      "has spend time 108m 27s/n\n",
      "val Loss: 0.5551 Acc: 0.7059\n",
      "has spend time 108m 27s/n\n",
      "\n",
      "Epoch 3053/9999\n",
      "----------\n",
      "train Loss: 0.4917 Acc: 0.7418\n",
      "has spend time 108m 29s/n\n",
      "val Loss: 0.5442 Acc: 0.7059\n",
      "has spend time 108m 29s/n\n",
      "\n",
      "Epoch 3054/9999\n",
      "----------\n",
      "train Loss: 0.5466 Acc: 0.7090\n",
      "has spend time 108m 31s/n\n",
      "val Loss: 0.5588 Acc: 0.6993\n",
      "has spend time 108m 31s/n\n",
      "\n",
      "Epoch 3055/9999\n",
      "----------\n",
      "train Loss: 0.5196 Acc: 0.7500\n",
      "has spend time 108m 33s/n\n",
      "val Loss: 0.5795 Acc: 0.6928\n",
      "has spend time 108m 33s/n\n",
      "\n",
      "Epoch 3056/9999\n",
      "----------\n",
      "train Loss: 0.4977 Acc: 0.7459\n",
      "has spend time 108m 35s/n\n",
      "val Loss: 0.5626 Acc: 0.6928\n",
      "has spend time 108m 36s/n\n",
      "\n",
      "Epoch 3057/9999\n",
      "----------\n",
      "train Loss: 0.4824 Acc: 0.7787\n",
      "has spend time 108m 37s/n\n",
      "val Loss: 0.5541 Acc: 0.6993\n",
      "has spend time 108m 38s/n\n",
      "\n",
      "Epoch 3058/9999\n",
      "----------\n",
      "train Loss: 0.5160 Acc: 0.7541\n",
      "has spend time 108m 39s/n\n",
      "val Loss: 0.5397 Acc: 0.7190\n",
      "has spend time 108m 40s/n\n",
      "\n",
      "Epoch 3059/9999\n",
      "----------\n",
      "train Loss: 0.5460 Acc: 0.7049\n",
      "has spend time 108m 41s/n\n",
      "val Loss: 0.5465 Acc: 0.7190\n",
      "has spend time 108m 42s/n\n",
      "\n",
      "Epoch 3060/9999\n",
      "----------\n",
      "train Loss: 0.5178 Acc: 0.7090\n",
      "has spend time 108m 43s/n\n",
      "val Loss: 0.5488 Acc: 0.7124\n",
      "has spend time 108m 44s/n\n",
      "\n",
      "Epoch 3061/9999\n",
      "----------\n",
      "train Loss: 0.4906 Acc: 0.7705\n",
      "has spend time 108m 45s/n\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val Loss: 0.5403 Acc: 0.7255\n",
      "has spend time 108m 46s/n\n",
      "\n",
      "Epoch 3062/9999\n",
      "----------\n",
      "train Loss: 0.4887 Acc: 0.7664\n",
      "has spend time 108m 48s/n\n",
      "val Loss: 0.5448 Acc: 0.7190\n",
      "has spend time 108m 48s/n\n",
      "\n",
      "Epoch 3063/9999\n",
      "----------\n",
      "train Loss: 0.5118 Acc: 0.7131\n",
      "has spend time 108m 50s/n\n",
      "val Loss: 0.5582 Acc: 0.6928\n",
      "has spend time 108m 50s/n\n",
      "\n",
      "Epoch 3064/9999\n",
      "----------\n",
      "train Loss: 0.4918 Acc: 0.7213\n",
      "has spend time 108m 52s/n\n",
      "val Loss: 0.5545 Acc: 0.7059\n",
      "has spend time 108m 52s/n\n",
      "\n",
      "Epoch 3065/9999\n",
      "----------\n",
      "train Loss: 0.4762 Acc: 0.7664\n",
      "has spend time 108m 54s/n\n",
      "val Loss: 0.5536 Acc: 0.7124\n",
      "has spend time 108m 54s/n\n",
      "\n",
      "Epoch 3066/9999\n",
      "----------\n",
      "train Loss: 0.5591 Acc: 0.6803\n",
      "has spend time 108m 56s/n\n",
      "val Loss: 0.5453 Acc: 0.7255\n",
      "has spend time 108m 56s/n\n",
      "\n",
      "Epoch 3067/9999\n",
      "----------\n",
      "train Loss: 0.5047 Acc: 0.7582\n",
      "has spend time 108m 58s/n\n",
      "val Loss: 0.5515 Acc: 0.7059\n",
      "has spend time 108m 59s/n\n",
      "\n",
      "Epoch 3068/9999\n",
      "----------\n",
      "train Loss: 0.5363 Acc: 0.7008\n",
      "has spend time 109m 0s/n\n",
      "val Loss: 0.5544 Acc: 0.6993\n",
      "has spend time 109m 1s/n\n",
      "\n",
      "Epoch 3069/9999\n",
      "----------\n",
      "train Loss: 0.4915 Acc: 0.7787\n",
      "has spend time 109m 3s/n\n",
      "val Loss: 0.5489 Acc: 0.7124\n",
      "has spend time 109m 3s/n\n",
      "\n",
      "Epoch 3070/9999\n",
      "----------\n",
      "train Loss: 0.4927 Acc: 0.7459\n",
      "has spend time 109m 5s/n\n",
      "val Loss: 0.5450 Acc: 0.6993\n",
      "has spend time 109m 5s/n\n",
      "\n",
      "Epoch 3071/9999\n",
      "----------\n",
      "train Loss: 0.4985 Acc: 0.7377\n",
      "has spend time 109m 7s/n\n",
      "val Loss: 0.5601 Acc: 0.6993\n",
      "has spend time 109m 7s/n\n",
      "\n",
      "Epoch 3072/9999\n",
      "----------\n",
      "train Loss: 0.5467 Acc: 0.7377\n",
      "has spend time 109m 9s/n\n",
      "val Loss: 0.5588 Acc: 0.7059\n",
      "has spend time 109m 9s/n\n",
      "\n",
      "Epoch 3073/9999\n",
      "----------\n",
      "train Loss: 0.4801 Acc: 0.7418\n",
      "has spend time 109m 11s/n\n",
      "val Loss: 0.5462 Acc: 0.7124\n",
      "has spend time 109m 11s/n\n",
      "\n",
      "Epoch 3074/9999\n",
      "----------\n",
      "train Loss: 0.5413 Acc: 0.7008\n",
      "has spend time 109m 13s/n\n",
      "val Loss: 0.5506 Acc: 0.7190\n",
      "has spend time 109m 14s/n\n",
      "\n",
      "Epoch 3075/9999\n",
      "----------\n",
      "train Loss: 0.4987 Acc: 0.7582\n",
      "has spend time 109m 15s/n\n",
      "val Loss: 0.5393 Acc: 0.7059\n",
      "has spend time 109m 16s/n\n",
      "\n",
      "Epoch 3076/9999\n",
      "----------\n",
      "train Loss: 0.5379 Acc: 0.7213\n",
      "has spend time 109m 18s/n\n",
      "val Loss: 0.5426 Acc: 0.7255\n",
      "has spend time 109m 18s/n\n",
      "\n",
      "Epoch 3077/9999\n",
      "----------\n",
      "train Loss: 0.5234 Acc: 0.7172\n",
      "has spend time 109m 20s/n\n",
      "val Loss: 0.5429 Acc: 0.7124\n",
      "has spend time 109m 20s/n\n",
      "\n",
      "Epoch 3078/9999\n",
      "----------\n",
      "train Loss: 0.5541 Acc: 0.7172\n",
      "has spend time 109m 22s/n\n",
      "val Loss: 0.5552 Acc: 0.6993\n",
      "has spend time 109m 23s/n\n",
      "\n",
      "Epoch 3079/9999\n",
      "----------\n",
      "train Loss: 0.5130 Acc: 0.7336\n",
      "has spend time 109m 24s/n\n",
      "val Loss: 0.5491 Acc: 0.7059\n",
      "has spend time 109m 25s/n\n",
      "\n",
      "Epoch 3080/9999\n",
      "----------\n",
      "train Loss: 0.4848 Acc: 0.7582\n",
      "has spend time 109m 26s/n\n",
      "val Loss: 0.5552 Acc: 0.6928\n",
      "has spend time 109m 27s/n\n",
      "\n",
      "Epoch 3081/9999\n",
      "----------\n",
      "train Loss: 0.5041 Acc: 0.7254\n",
      "has spend time 109m 28s/n\n",
      "val Loss: 0.5486 Acc: 0.6928\n",
      "has spend time 109m 29s/n\n",
      "\n",
      "Epoch 3082/9999\n",
      "----------\n",
      "train Loss: 0.4957 Acc: 0.7459\n",
      "has spend time 109m 31s/n\n",
      "val Loss: 0.5498 Acc: 0.7059\n",
      "has spend time 109m 31s/n\n",
      "\n",
      "Epoch 3083/9999\n",
      "----------\n",
      "train Loss: 0.4937 Acc: 0.7623\n",
      "has spend time 109m 33s/n\n",
      "val Loss: 0.5428 Acc: 0.7059\n",
      "has spend time 109m 33s/n\n",
      "\n",
      "Epoch 3084/9999\n",
      "----------\n",
      "train Loss: 0.4988 Acc: 0.7377\n",
      "has spend time 109m 35s/n\n",
      "val Loss: 0.5483 Acc: 0.7190\n",
      "has spend time 109m 35s/n\n",
      "\n",
      "Epoch 3085/9999\n",
      "----------\n",
      "train Loss: 0.5484 Acc: 0.7049\n",
      "has spend time 109m 37s/n\n",
      "val Loss: 0.5493 Acc: 0.7190\n",
      "has spend time 109m 37s/n\n",
      "\n",
      "Epoch 3086/9999\n",
      "----------\n",
      "train Loss: 0.5526 Acc: 0.6926\n",
      "has spend time 109m 39s/n\n",
      "val Loss: 0.5541 Acc: 0.7255\n",
      "has spend time 109m 40s/n\n",
      "\n",
      "Epoch 3087/9999\n",
      "----------\n",
      "train Loss: 0.5134 Acc: 0.7787\n",
      "has spend time 109m 41s/n\n",
      "val Loss: 0.5477 Acc: 0.7059\n",
      "has spend time 109m 42s/n\n",
      "\n",
      "Epoch 3088/9999\n",
      "----------\n",
      "train Loss: 0.5034 Acc: 0.7336\n",
      "has spend time 109m 43s/n\n",
      "val Loss: 0.5541 Acc: 0.6993\n",
      "has spend time 109m 44s/n\n",
      "\n",
      "Epoch 3089/9999\n",
      "----------\n",
      "train Loss: 0.5310 Acc: 0.7295\n",
      "has spend time 109m 45s/n\n",
      "val Loss: 0.5437 Acc: 0.7190\n",
      "has spend time 109m 46s/n\n",
      "\n",
      "Epoch 3090/9999\n",
      "----------\n",
      "train Loss: 0.4851 Acc: 0.7459\n",
      "has spend time 109m 47s/n\n",
      "val Loss: 0.5464 Acc: 0.7190\n",
      "has spend time 109m 48s/n\n",
      "\n",
      "Epoch 3091/9999\n",
      "----------\n",
      "train Loss: 0.5331 Acc: 0.7131\n",
      "has spend time 109m 49s/n\n",
      "val Loss: 0.5455 Acc: 0.7190\n",
      "has spend time 109m 50s/n\n",
      "\n",
      "Epoch 3092/9999\n",
      "----------\n",
      "train Loss: 0.5003 Acc: 0.7541\n",
      "has spend time 109m 52s/n\n",
      "val Loss: 0.5461 Acc: 0.7124\n",
      "has spend time 109m 52s/n\n",
      "\n",
      "Epoch 3093/9999\n",
      "----------\n",
      "train Loss: 0.4700 Acc: 0.7623\n",
      "has spend time 109m 54s/n\n",
      "val Loss: 0.5449 Acc: 0.7255\n",
      "has spend time 109m 54s/n\n",
      "\n",
      "Epoch 3094/9999\n",
      "----------\n",
      "train Loss: 0.4991 Acc: 0.7418\n",
      "has spend time 109m 56s/n\n",
      "val Loss: 0.5461 Acc: 0.7059\n",
      "has spend time 109m 56s/n\n",
      "\n",
      "Epoch 3095/9999\n",
      "----------\n",
      "train Loss: 0.4900 Acc: 0.7582\n",
      "has spend time 109m 58s/n\n",
      "val Loss: 0.5587 Acc: 0.6993\n",
      "has spend time 109m 59s/n\n",
      "\n",
      "Epoch 3096/9999\n",
      "----------\n",
      "train Loss: 0.5089 Acc: 0.7459\n",
      "has spend time 110m 0s/n\n",
      "val Loss: 0.5466 Acc: 0.7059\n",
      "has spend time 110m 1s/n\n",
      "\n",
      "Epoch 3097/9999\n",
      "----------\n",
      "train Loss: 0.5107 Acc: 0.7541\n",
      "has spend time 110m 2s/n\n",
      "val Loss: 0.5453 Acc: 0.7059\n",
      "has spend time 110m 3s/n\n",
      "\n",
      "Epoch 3098/9999\n",
      "----------\n",
      "train Loss: 0.5083 Acc: 0.7377\n",
      "has spend time 110m 5s/n\n",
      "val Loss: 0.5542 Acc: 0.6928\n",
      "has spend time 110m 5s/n\n",
      "\n",
      "Epoch 3099/9999\n",
      "----------\n",
      "train Loss: 0.5238 Acc: 0.7254\n",
      "has spend time 110m 7s/n\n",
      "val Loss: 0.5562 Acc: 0.7059\n",
      "has spend time 110m 7s/n\n",
      "\n",
      "Epoch 3100/9999\n",
      "----------\n",
      "train Loss: 0.4898 Acc: 0.7705\n",
      "has spend time 110m 9s/n\n",
      "val Loss: 0.5448 Acc: 0.7124\n",
      "has spend time 110m 9s/n\n",
      "\n",
      "Epoch 3101/9999\n",
      "----------\n",
      "train Loss: 0.5123 Acc: 0.7418\n",
      "has spend time 110m 11s/n\n",
      "val Loss: 0.5476 Acc: 0.7190\n",
      "has spend time 110m 11s/n\n",
      "\n",
      "Epoch 3102/9999\n",
      "----------\n",
      "train Loss: 0.5142 Acc: 0.7418\n",
      "has spend time 110m 13s/n\n",
      "val Loss: 0.5439 Acc: 0.7059\n",
      "has spend time 110m 14s/n\n",
      "\n",
      "Epoch 3103/9999\n",
      "----------\n",
      "train Loss: 0.5154 Acc: 0.7172\n",
      "has spend time 110m 15s/n\n",
      "val Loss: 0.5506 Acc: 0.7124\n",
      "has spend time 110m 16s/n\n",
      "\n",
      "Epoch 3104/9999\n",
      "----------\n",
      "train Loss: 0.5281 Acc: 0.7295\n",
      "has spend time 110m 17s/n\n",
      "val Loss: 0.5423 Acc: 0.7124\n",
      "has spend time 110m 18s/n\n",
      "\n",
      "Epoch 3105/9999\n",
      "----------\n",
      "train Loss: 0.4990 Acc: 0.7787\n",
      "has spend time 110m 19s/n\n",
      "val Loss: 0.5460 Acc: 0.7059\n",
      "has spend time 110m 20s/n\n",
      "\n",
      "Epoch 3106/9999\n",
      "----------\n",
      "train Loss: 0.5221 Acc: 0.7213\n",
      "has spend time 110m 22s/n\n",
      "val Loss: 0.5411 Acc: 0.7190\n",
      "has spend time 110m 23s/n\n",
      "\n",
      "Epoch 3107/9999\n",
      "----------\n",
      "train Loss: 0.5397 Acc: 0.7049\n",
      "has spend time 110m 24s/n\n",
      "val Loss: 0.5417 Acc: 0.7190\n",
      "has spend time 110m 25s/n\n",
      "\n",
      "Epoch 3108/9999\n",
      "----------\n",
      "train Loss: 0.5064 Acc: 0.7336\n",
      "has spend time 110m 26s/n\n",
      "val Loss: 0.5481 Acc: 0.7255\n",
      "has spend time 110m 27s/n\n",
      "\n",
      "Epoch 3109/9999\n",
      "----------\n",
      "train Loss: 0.5269 Acc: 0.6926\n",
      "has spend time 110m 28s/n\n",
      "val Loss: 0.5470 Acc: 0.7124\n",
      "has spend time 110m 29s/n\n",
      "\n",
      "Epoch 3110/9999\n",
      "----------\n",
      "train Loss: 0.4831 Acc: 0.7787\n",
      "has spend time 110m 30s/n\n",
      "val Loss: 0.5499 Acc: 0.7059\n",
      "has spend time 110m 31s/n\n",
      "\n",
      "Epoch 3111/9999\n",
      "----------\n",
      "train Loss: 0.5074 Acc: 0.7377\n",
      "has spend time 110m 32s/n\n",
      "val Loss: 0.5457 Acc: 0.7255\n",
      "has spend time 110m 33s/n\n",
      "\n",
      "Epoch 3112/9999\n",
      "----------\n",
      "train Loss: 0.5221 Acc: 0.7172\n",
      "has spend time 110m 35s/n\n",
      "val Loss: 0.5427 Acc: 0.7255\n",
      "has spend time 110m 36s/n\n",
      "\n",
      "Epoch 3113/9999\n",
      "----------\n",
      "train Loss: 0.4934 Acc: 0.7254\n",
      "has spend time 110m 37s/n\n",
      "val Loss: 0.5366 Acc: 0.7255\n",
      "has spend time 110m 38s/n\n",
      "\n",
      "Epoch 3114/9999\n",
      "----------\n",
      "train Loss: 0.4997 Acc: 0.7418\n",
      "has spend time 110m 39s/n\n",
      "val Loss: 0.5425 Acc: 0.7059\n",
      "has spend time 110m 40s/n\n",
      "\n",
      "Epoch 3115/9999\n",
      "----------\n",
      "train Loss: 0.4994 Acc: 0.7295\n",
      "has spend time 110m 42s/n\n",
      "val Loss: 0.5388 Acc: 0.7190\n",
      "has spend time 110m 42s/n\n",
      "\n",
      "Epoch 3116/9999\n",
      "----------\n",
      "train Loss: 0.4924 Acc: 0.7295\n",
      "has spend time 110m 44s/n\n",
      "val Loss: 0.5458 Acc: 0.7124\n",
      "has spend time 110m 44s/n\n",
      "\n",
      "Epoch 3117/9999\n",
      "----------\n",
      "train Loss: 0.5285 Acc: 0.7295\n",
      "has spend time 110m 46s/n\n",
      "val Loss: 0.5546 Acc: 0.7059\n",
      "has spend time 110m 46s/n\n",
      "\n",
      "Epoch 3118/9999\n",
      "----------\n",
      "train Loss: 0.5091 Acc: 0.7254\n",
      "has spend time 110m 48s/n\n",
      "val Loss: 0.5558 Acc: 0.7059\n",
      "has spend time 110m 48s/n\n",
      "\n",
      "Epoch 3119/9999\n",
      "----------\n",
      "train Loss: 0.5459 Acc: 0.6885\n",
      "has spend time 110m 50s/n\n",
      "val Loss: 0.5668 Acc: 0.6993\n",
      "has spend time 110m 50s/n\n",
      "\n",
      "Epoch 3120/9999\n",
      "----------\n",
      "train Loss: 0.5038 Acc: 0.7459\n",
      "has spend time 110m 52s/n\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val Loss: 0.5525 Acc: 0.7124\n",
      "has spend time 110m 52s/n\n",
      "\n",
      "Epoch 3121/9999\n",
      "----------\n",
      "train Loss: 0.5086 Acc: 0.7459\n",
      "has spend time 110m 54s/n\n",
      "val Loss: 0.5484 Acc: 0.7124\n",
      "has spend time 110m 55s/n\n",
      "\n",
      "Epoch 3122/9999\n",
      "----------\n",
      "train Loss: 0.4978 Acc: 0.7582\n",
      "has spend time 110m 56s/n\n",
      "val Loss: 0.5382 Acc: 0.7124\n",
      "has spend time 110m 57s/n\n",
      "\n",
      "Epoch 3123/9999\n",
      "----------\n",
      "train Loss: 0.5144 Acc: 0.7049\n",
      "has spend time 110m 59s/n\n",
      "val Loss: 0.5432 Acc: 0.7124\n",
      "has spend time 110m 59s/n\n",
      "\n",
      "Epoch 3124/9999\n",
      "----------\n",
      "train Loss: 0.5404 Acc: 0.7295\n",
      "has spend time 111m 1s/n\n",
      "val Loss: 0.5461 Acc: 0.7059\n",
      "has spend time 111m 1s/n\n",
      "\n",
      "Epoch 3125/9999\n",
      "----------\n",
      "train Loss: 0.5107 Acc: 0.7254\n",
      "has spend time 111m 3s/n\n",
      "val Loss: 0.5521 Acc: 0.6993\n",
      "has spend time 111m 3s/n\n",
      "\n",
      "Epoch 3126/9999\n",
      "----------\n",
      "train Loss: 0.4947 Acc: 0.7500\n",
      "has spend time 111m 5s/n\n",
      "val Loss: 0.5441 Acc: 0.7124\n",
      "has spend time 111m 5s/n\n",
      "\n",
      "Epoch 3127/9999\n",
      "----------\n",
      "train Loss: 0.5183 Acc: 0.7541\n",
      "has spend time 111m 7s/n\n",
      "val Loss: 0.5481 Acc: 0.7059\n",
      "has spend time 111m 7s/n\n",
      "\n",
      "Epoch 3128/9999\n",
      "----------\n",
      "train Loss: 0.4904 Acc: 0.7336\n",
      "has spend time 111m 9s/n\n",
      "val Loss: 0.5450 Acc: 0.7190\n",
      "has spend time 111m 9s/n\n",
      "\n",
      "Epoch 3129/9999\n",
      "----------\n",
      "train Loss: 0.5155 Acc: 0.7295\n",
      "has spend time 111m 11s/n\n",
      "val Loss: 0.5503 Acc: 0.7124\n",
      "has spend time 111m 12s/n\n",
      "\n",
      "Epoch 3130/9999\n",
      "----------\n",
      "train Loss: 0.5131 Acc: 0.7500\n",
      "has spend time 111m 13s/n\n",
      "val Loss: 0.5385 Acc: 0.7124\n",
      "has spend time 111m 14s/n\n",
      "\n",
      "Epoch 3131/9999\n",
      "----------\n",
      "train Loss: 0.4894 Acc: 0.7705\n",
      "has spend time 111m 16s/n\n",
      "val Loss: 0.5564 Acc: 0.6993\n",
      "has spend time 111m 16s/n\n",
      "\n",
      "Epoch 3132/9999\n",
      "----------\n",
      "train Loss: 0.5146 Acc: 0.7049\n",
      "has spend time 111m 18s/n\n",
      "val Loss: 0.5480 Acc: 0.7190\n",
      "has spend time 111m 19s/n\n",
      "\n",
      "Epoch 3133/9999\n",
      "----------\n",
      "train Loss: 0.5166 Acc: 0.7336\n",
      "has spend time 111m 20s/n\n",
      "val Loss: 0.5577 Acc: 0.6993\n",
      "has spend time 111m 21s/n\n",
      "\n",
      "Epoch 3134/9999\n",
      "----------\n",
      "train Loss: 0.5009 Acc: 0.7664\n",
      "has spend time 111m 22s/n\n",
      "val Loss: 0.5554 Acc: 0.7059\n",
      "has spend time 111m 23s/n\n",
      "\n",
      "Epoch 3135/9999\n",
      "----------\n",
      "train Loss: 0.4968 Acc: 0.7418\n",
      "has spend time 111m 24s/n\n",
      "val Loss: 0.5579 Acc: 0.7059\n",
      "has spend time 111m 25s/n\n",
      "\n",
      "Epoch 3136/9999\n",
      "----------\n",
      "train Loss: 0.5414 Acc: 0.7049\n",
      "has spend time 111m 26s/n\n",
      "val Loss: 0.5444 Acc: 0.7255\n",
      "has spend time 111m 27s/n\n",
      "\n",
      "Epoch 3137/9999\n",
      "----------\n",
      "train Loss: 0.4989 Acc: 0.7336\n",
      "has spend time 111m 29s/n\n",
      "val Loss: 0.5486 Acc: 0.7190\n",
      "has spend time 111m 29s/n\n",
      "\n",
      "Epoch 3138/9999\n",
      "----------\n",
      "train Loss: 0.4940 Acc: 0.7582\n",
      "has spend time 111m 31s/n\n",
      "val Loss: 0.5344 Acc: 0.7255\n",
      "has spend time 111m 31s/n\n",
      "\n",
      "Epoch 3139/9999\n",
      "----------\n",
      "train Loss: 0.4833 Acc: 0.7418\n",
      "has spend time 111m 33s/n\n",
      "val Loss: 0.5402 Acc: 0.7190\n",
      "has spend time 111m 33s/n\n",
      "\n",
      "Epoch 3140/9999\n",
      "----------\n",
      "train Loss: 0.4863 Acc: 0.7541\n",
      "has spend time 111m 35s/n\n",
      "val Loss: 0.5542 Acc: 0.6993\n",
      "has spend time 111m 35s/n\n",
      "\n",
      "Epoch 3141/9999\n",
      "----------\n",
      "train Loss: 0.5290 Acc: 0.7254\n",
      "has spend time 111m 37s/n\n",
      "val Loss: 0.5446 Acc: 0.7190\n",
      "has spend time 111m 37s/n\n",
      "\n",
      "Epoch 3142/9999\n",
      "----------\n",
      "train Loss: 0.5031 Acc: 0.7295\n",
      "has spend time 111m 39s/n\n",
      "val Loss: 0.5569 Acc: 0.7059\n",
      "has spend time 111m 39s/n\n",
      "\n",
      "Epoch 3143/9999\n",
      "----------\n",
      "train Loss: 0.5263 Acc: 0.7295\n",
      "has spend time 111m 41s/n\n",
      "val Loss: 0.5545 Acc: 0.7059\n",
      "has spend time 111m 41s/n\n",
      "\n",
      "Epoch 3144/9999\n",
      "----------\n",
      "train Loss: 0.5326 Acc: 0.7213\n",
      "has spend time 111m 43s/n\n",
      "val Loss: 0.5562 Acc: 0.7124\n",
      "has spend time 111m 43s/n\n",
      "\n",
      "Epoch 3145/9999\n",
      "----------\n",
      "train Loss: 0.5078 Acc: 0.7172\n",
      "has spend time 111m 45s/n\n",
      "val Loss: 0.5552 Acc: 0.6863\n",
      "has spend time 111m 45s/n\n",
      "\n",
      "Epoch 3146/9999\n",
      "----------\n",
      "train Loss: 0.5162 Acc: 0.7418\n",
      "has spend time 111m 47s/n\n",
      "val Loss: 0.5566 Acc: 0.6928\n",
      "has spend time 111m 48s/n\n",
      "\n",
      "Epoch 3147/9999\n",
      "----------\n",
      "train Loss: 0.4902 Acc: 0.7336\n",
      "has spend time 111m 49s/n\n",
      "val Loss: 0.5485 Acc: 0.7124\n",
      "has spend time 111m 50s/n\n",
      "\n",
      "Epoch 3148/9999\n",
      "----------\n",
      "train Loss: 0.5251 Acc: 0.7541\n",
      "has spend time 111m 52s/n\n",
      "val Loss: 0.5577 Acc: 0.7059\n",
      "has spend time 111m 52s/n\n",
      "\n",
      "Epoch 3149/9999\n",
      "----------\n",
      "train Loss: 0.4969 Acc: 0.7582\n",
      "has spend time 111m 54s/n\n",
      "val Loss: 0.5548 Acc: 0.6993\n",
      "has spend time 111m 54s/n\n",
      "\n",
      "Epoch 3150/9999\n",
      "----------\n",
      "train Loss: 0.5227 Acc: 0.7049\n",
      "has spend time 111m 56s/n\n",
      "val Loss: 0.5616 Acc: 0.6993\n",
      "has spend time 111m 56s/n\n",
      "\n",
      "Epoch 3151/9999\n",
      "----------\n",
      "train Loss: 0.4972 Acc: 0.7582\n",
      "has spend time 111m 58s/n\n",
      "val Loss: 0.5448 Acc: 0.7255\n",
      "has spend time 111m 59s/n\n",
      "\n",
      "Epoch 3152/9999\n",
      "----------\n",
      "train Loss: 0.5061 Acc: 0.7500\n",
      "has spend time 112m 0s/n\n",
      "val Loss: 0.5609 Acc: 0.7059\n",
      "has spend time 112m 1s/n\n",
      "\n",
      "Epoch 3153/9999\n",
      "----------\n",
      "train Loss: 0.5280 Acc: 0.7172\n",
      "has spend time 112m 2s/n\n",
      "val Loss: 0.5580 Acc: 0.6928\n",
      "has spend time 112m 3s/n\n",
      "\n",
      "Epoch 3154/9999\n",
      "----------\n",
      "train Loss: 0.5331 Acc: 0.6844\n",
      "has spend time 112m 4s/n\n",
      "val Loss: 0.5513 Acc: 0.7190\n",
      "has spend time 112m 5s/n\n",
      "\n",
      "Epoch 3155/9999\n",
      "----------\n",
      "train Loss: 0.5340 Acc: 0.7418\n",
      "has spend time 112m 7s/n\n",
      "val Loss: 0.5550 Acc: 0.7059\n",
      "has spend time 112m 7s/n\n",
      "\n",
      "Epoch 3156/9999\n",
      "----------\n",
      "train Loss: 0.4886 Acc: 0.7459\n",
      "has spend time 112m 9s/n\n",
      "val Loss: 0.5513 Acc: 0.6993\n",
      "has spend time 112m 9s/n\n",
      "\n",
      "Epoch 3157/9999\n",
      "----------\n",
      "train Loss: 0.4901 Acc: 0.7418\n",
      "has spend time 112m 11s/n\n",
      "val Loss: 0.5587 Acc: 0.6993\n",
      "has spend time 112m 11s/n\n",
      "\n",
      "Epoch 3158/9999\n",
      "----------\n",
      "train Loss: 0.5170 Acc: 0.7008\n",
      "has spend time 112m 13s/n\n",
      "val Loss: 0.5493 Acc: 0.7124\n",
      "has spend time 112m 13s/n\n",
      "\n",
      "Epoch 3159/9999\n",
      "----------\n",
      "train Loss: 0.5213 Acc: 0.7418\n",
      "has spend time 112m 15s/n\n",
      "val Loss: 0.5477 Acc: 0.7124\n",
      "has spend time 112m 16s/n\n",
      "\n",
      "Epoch 3160/9999\n",
      "----------\n",
      "train Loss: 0.5109 Acc: 0.7459\n",
      "has spend time 112m 17s/n\n",
      "val Loss: 0.5541 Acc: 0.7124\n",
      "has spend time 112m 18s/n\n",
      "\n",
      "Epoch 3161/9999\n",
      "----------\n",
      "train Loss: 0.5067 Acc: 0.7418\n",
      "has spend time 112m 19s/n\n",
      "val Loss: 0.5649 Acc: 0.6993\n",
      "has spend time 112m 20s/n\n",
      "\n",
      "Epoch 3162/9999\n",
      "----------\n",
      "train Loss: 0.5038 Acc: 0.7336\n",
      "has spend time 112m 22s/n\n",
      "val Loss: 0.5469 Acc: 0.7124\n",
      "has spend time 112m 22s/n\n",
      "\n",
      "Epoch 3163/9999\n",
      "----------\n",
      "train Loss: 0.4878 Acc: 0.7746\n",
      "has spend time 112m 24s/n\n",
      "val Loss: 0.5407 Acc: 0.7190\n",
      "has spend time 112m 24s/n\n",
      "\n",
      "Epoch 3164/9999\n",
      "----------\n",
      "train Loss: 0.5075 Acc: 0.7541\n",
      "has spend time 112m 26s/n\n",
      "val Loss: 0.5623 Acc: 0.6993\n",
      "has spend time 112m 26s/n\n",
      "\n",
      "Epoch 3165/9999\n",
      "----------\n",
      "train Loss: 0.4755 Acc: 0.7500\n",
      "has spend time 112m 28s/n\n",
      "val Loss: 0.5515 Acc: 0.6993\n",
      "has spend time 112m 28s/n\n",
      "\n",
      "Epoch 3166/9999\n",
      "----------\n",
      "train Loss: 0.5278 Acc: 0.7336\n",
      "has spend time 112m 30s/n\n",
      "val Loss: 0.5546 Acc: 0.7059\n",
      "has spend time 112m 30s/n\n",
      "\n",
      "Epoch 3167/9999\n",
      "----------\n",
      "train Loss: 0.5131 Acc: 0.7213\n",
      "has spend time 112m 32s/n\n",
      "val Loss: 0.5488 Acc: 0.7059\n",
      "has spend time 112m 32s/n\n",
      "\n",
      "Epoch 3168/9999\n",
      "----------\n",
      "train Loss: 0.4989 Acc: 0.7418\n",
      "has spend time 112m 34s/n\n",
      "val Loss: 0.5618 Acc: 0.6993\n",
      "has spend time 112m 34s/n\n",
      "\n",
      "Epoch 3169/9999\n",
      "----------\n",
      "train Loss: 0.5293 Acc: 0.7090\n",
      "has spend time 112m 36s/n\n",
      "val Loss: 0.5480 Acc: 0.7124\n",
      "has spend time 112m 36s/n\n",
      "\n",
      "Epoch 3170/9999\n",
      "----------\n",
      "train Loss: 0.5035 Acc: 0.7705\n",
      "has spend time 112m 38s/n\n",
      "val Loss: 0.5720 Acc: 0.7059\n",
      "has spend time 112m 39s/n\n",
      "\n",
      "Epoch 3171/9999\n",
      "----------\n",
      "train Loss: 0.4919 Acc: 0.7582\n",
      "has spend time 112m 40s/n\n",
      "val Loss: 0.5497 Acc: 0.6993\n",
      "has spend time 112m 41s/n\n",
      "\n",
      "Epoch 3172/9999\n",
      "----------\n",
      "train Loss: 0.5127 Acc: 0.7664\n",
      "has spend time 112m 42s/n\n",
      "val Loss: 0.5562 Acc: 0.6928\n",
      "has spend time 112m 43s/n\n",
      "\n",
      "Epoch 3173/9999\n",
      "----------\n",
      "train Loss: 0.5053 Acc: 0.7254\n",
      "has spend time 112m 44s/n\n",
      "val Loss: 0.5487 Acc: 0.7124\n",
      "has spend time 112m 45s/n\n",
      "\n",
      "Epoch 3174/9999\n",
      "----------\n",
      "train Loss: 0.4968 Acc: 0.7541\n",
      "has spend time 112m 47s/n\n",
      "val Loss: 0.5389 Acc: 0.7190\n",
      "has spend time 112m 47s/n\n",
      "\n",
      "Epoch 3175/9999\n",
      "----------\n",
      "train Loss: 0.5271 Acc: 0.6967\n",
      "has spend time 112m 49s/n\n",
      "val Loss: 0.5439 Acc: 0.7124\n",
      "has spend time 112m 49s/n\n",
      "\n",
      "Epoch 3176/9999\n",
      "----------\n",
      "train Loss: 0.4723 Acc: 0.7787\n",
      "has spend time 112m 51s/n\n",
      "val Loss: 0.5551 Acc: 0.6928\n",
      "has spend time 112m 51s/n\n",
      "\n",
      "Epoch 3177/9999\n",
      "----------\n",
      "train Loss: 0.5098 Acc: 0.7131\n",
      "has spend time 112m 53s/n\n",
      "val Loss: 0.5419 Acc: 0.7190\n",
      "has spend time 112m 54s/n\n",
      "\n",
      "Epoch 3178/9999\n",
      "----------\n",
      "train Loss: 0.5241 Acc: 0.6885\n",
      "has spend time 112m 55s/n\n",
      "val Loss: 0.5539 Acc: 0.7124\n",
      "has spend time 112m 56s/n\n",
      "\n",
      "Epoch 3179/9999\n",
      "----------\n",
      "train Loss: 0.4935 Acc: 0.7746\n",
      "has spend time 112m 58s/n\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val Loss: 0.5439 Acc: 0.7190\n",
      "has spend time 112m 58s/n\n",
      "\n",
      "Epoch 3180/9999\n",
      "----------\n",
      "train Loss: 0.4899 Acc: 0.7664\n",
      "has spend time 112m 60s/n\n",
      "val Loss: 0.5432 Acc: 0.7124\n",
      "has spend time 113m 1s/n\n",
      "\n",
      "Epoch 3181/9999\n",
      "----------\n",
      "train Loss: 0.4869 Acc: 0.7705\n",
      "has spend time 113m 2s/n\n",
      "val Loss: 0.5471 Acc: 0.7124\n",
      "has spend time 113m 3s/n\n",
      "\n",
      "Epoch 3182/9999\n",
      "----------\n",
      "train Loss: 0.4955 Acc: 0.7705\n",
      "has spend time 113m 4s/n\n",
      "val Loss: 0.5521 Acc: 0.7124\n",
      "has spend time 113m 5s/n\n",
      "\n",
      "Epoch 3183/9999\n",
      "----------\n",
      "train Loss: 0.5019 Acc: 0.7623\n",
      "has spend time 113m 6s/n\n",
      "val Loss: 0.5482 Acc: 0.7124\n",
      "has spend time 113m 7s/n\n",
      "\n",
      "Epoch 3184/9999\n",
      "----------\n",
      "train Loss: 0.5047 Acc: 0.7336\n",
      "has spend time 113m 8s/n\n",
      "val Loss: 0.5516 Acc: 0.7124\n",
      "has spend time 113m 9s/n\n",
      "\n",
      "Epoch 3185/9999\n",
      "----------\n",
      "train Loss: 0.5252 Acc: 0.7582\n",
      "has spend time 113m 10s/n\n",
      "val Loss: 0.5571 Acc: 0.6993\n",
      "has spend time 113m 11s/n\n",
      "\n",
      "Epoch 3186/9999\n",
      "----------\n",
      "train Loss: 0.4931 Acc: 0.7500\n",
      "has spend time 113m 12s/n\n",
      "val Loss: 0.5505 Acc: 0.7059\n",
      "has spend time 113m 13s/n\n",
      "\n",
      "Epoch 3187/9999\n",
      "----------\n",
      "train Loss: 0.5042 Acc: 0.7295\n",
      "has spend time 113m 14s/n\n",
      "val Loss: 0.5811 Acc: 0.7059\n",
      "has spend time 113m 15s/n\n",
      "\n",
      "Epoch 3188/9999\n",
      "----------\n",
      "train Loss: 0.4754 Acc: 0.7869\n",
      "has spend time 113m 17s/n\n",
      "val Loss: 0.5732 Acc: 0.6928\n",
      "has spend time 113m 17s/n\n",
      "\n",
      "Epoch 3189/9999\n",
      "----------\n",
      "train Loss: 0.5037 Acc: 0.7459\n",
      "has spend time 113m 19s/n\n",
      "val Loss: 0.5474 Acc: 0.7255\n",
      "has spend time 113m 19s/n\n",
      "\n",
      "Epoch 3190/9999\n",
      "----------\n",
      "train Loss: 0.5168 Acc: 0.7131\n",
      "has spend time 113m 21s/n\n",
      "val Loss: 0.5487 Acc: 0.7059\n",
      "has spend time 113m 21s/n\n",
      "\n",
      "Epoch 3191/9999\n",
      "----------\n",
      "train Loss: 0.5165 Acc: 0.7254\n",
      "has spend time 113m 23s/n\n",
      "val Loss: 0.5477 Acc: 0.6993\n",
      "has spend time 113m 23s/n\n",
      "\n",
      "Epoch 3192/9999\n",
      "----------\n",
      "train Loss: 0.5397 Acc: 0.7213\n",
      "has spend time 113m 25s/n\n",
      "val Loss: 0.5396 Acc: 0.7124\n",
      "has spend time 113m 25s/n\n",
      "\n",
      "Epoch 3193/9999\n",
      "----------\n",
      "train Loss: 0.4906 Acc: 0.7459\n",
      "has spend time 113m 27s/n\n",
      "val Loss: 0.5541 Acc: 0.6993\n",
      "has spend time 113m 27s/n\n",
      "\n",
      "Epoch 3194/9999\n",
      "----------\n",
      "train Loss: 0.5613 Acc: 0.7172\n",
      "has spend time 113m 29s/n\n",
      "val Loss: 0.5457 Acc: 0.7190\n",
      "has spend time 113m 30s/n\n",
      "\n",
      "Epoch 3195/9999\n",
      "----------\n",
      "train Loss: 0.5392 Acc: 0.6803\n",
      "has spend time 113m 31s/n\n",
      "val Loss: 0.5636 Acc: 0.7059\n",
      "has spend time 113m 32s/n\n",
      "\n",
      "Epoch 3196/9999\n",
      "----------\n",
      "train Loss: 0.4862 Acc: 0.7664\n",
      "has spend time 113m 34s/n\n",
      "val Loss: 0.5556 Acc: 0.6993\n",
      "has spend time 113m 34s/n\n",
      "\n",
      "Epoch 3197/9999\n",
      "----------\n",
      "train Loss: 0.5073 Acc: 0.7459\n",
      "has spend time 113m 36s/n\n",
      "val Loss: 0.5575 Acc: 0.6928\n",
      "has spend time 113m 36s/n\n",
      "\n",
      "Epoch 3198/9999\n",
      "----------\n",
      "train Loss: 0.5018 Acc: 0.7500\n",
      "has spend time 113m 38s/n\n",
      "val Loss: 0.5556 Acc: 0.6863\n",
      "has spend time 113m 38s/n\n",
      "\n",
      "Epoch 3199/9999\n",
      "----------\n",
      "train Loss: 0.5200 Acc: 0.7418\n",
      "has spend time 113m 40s/n\n",
      "val Loss: 0.5618 Acc: 0.6732\n",
      "has spend time 113m 41s/n\n",
      "\n",
      "Epoch 3200/9999\n",
      "----------\n",
      "train Loss: 0.4842 Acc: 0.7418\n",
      "has spend time 113m 42s/n\n",
      "val Loss: 0.5659 Acc: 0.6993\n",
      "has spend time 113m 43s/n\n",
      "\n",
      "Epoch 3201/9999\n",
      "----------\n",
      "train Loss: 0.5153 Acc: 0.7172\n",
      "has spend time 113m 44s/n\n",
      "val Loss: 0.5471 Acc: 0.7124\n",
      "has spend time 113m 45s/n\n",
      "\n",
      "Epoch 3202/9999\n",
      "----------\n",
      "train Loss: 0.5125 Acc: 0.7090\n",
      "has spend time 113m 46s/n\n",
      "val Loss: 0.5409 Acc: 0.7124\n",
      "has spend time 113m 47s/n\n",
      "\n",
      "Epoch 3203/9999\n",
      "----------\n",
      "train Loss: 0.4821 Acc: 0.7623\n",
      "has spend time 113m 48s/n\n",
      "val Loss: 0.5421 Acc: 0.7190\n",
      "has spend time 113m 49s/n\n",
      "\n",
      "Epoch 3204/9999\n",
      "----------\n",
      "train Loss: 0.5167 Acc: 0.7418\n",
      "has spend time 113m 50s/n\n",
      "val Loss: 0.5495 Acc: 0.7124\n",
      "has spend time 113m 51s/n\n",
      "\n",
      "Epoch 3205/9999\n",
      "----------\n",
      "train Loss: 0.4896 Acc: 0.7541\n",
      "has spend time 113m 52s/n\n",
      "val Loss: 0.5780 Acc: 0.6928\n",
      "has spend time 113m 53s/n\n",
      "\n",
      "Epoch 3206/9999\n",
      "----------\n",
      "train Loss: 0.5381 Acc: 0.7049\n",
      "has spend time 113m 55s/n\n",
      "val Loss: 0.5536 Acc: 0.7059\n",
      "has spend time 113m 55s/n\n",
      "\n",
      "Epoch 3207/9999\n",
      "----------\n",
      "train Loss: 0.5040 Acc: 0.7295\n",
      "has spend time 113m 57s/n\n",
      "val Loss: 0.5490 Acc: 0.7124\n",
      "has spend time 113m 57s/n\n",
      "\n",
      "Epoch 3208/9999\n",
      "----------\n",
      "train Loss: 0.5154 Acc: 0.7377\n",
      "has spend time 113m 59s/n\n",
      "val Loss: 0.5448 Acc: 0.7059\n",
      "has spend time 113m 59s/n\n",
      "\n",
      "Epoch 3209/9999\n",
      "----------\n",
      "train Loss: 0.5004 Acc: 0.7459\n",
      "has spend time 114m 1s/n\n",
      "val Loss: 0.5510 Acc: 0.6993\n",
      "has spend time 114m 2s/n\n",
      "\n",
      "Epoch 3210/9999\n",
      "----------\n",
      "train Loss: 0.5125 Acc: 0.6844\n",
      "has spend time 114m 3s/n\n",
      "val Loss: 0.5425 Acc: 0.7059\n",
      "has spend time 114m 4s/n\n",
      "\n",
      "Epoch 3211/9999\n",
      "----------\n",
      "train Loss: 0.4992 Acc: 0.7459\n",
      "has spend time 114m 5s/n\n",
      "val Loss: 0.5474 Acc: 0.7124\n",
      "has spend time 114m 6s/n\n",
      "\n",
      "Epoch 3212/9999\n",
      "----------\n",
      "train Loss: 0.4995 Acc: 0.7418\n",
      "has spend time 114m 7s/n\n",
      "val Loss: 0.5523 Acc: 0.7124\n",
      "has spend time 114m 8s/n\n",
      "\n",
      "Epoch 3213/9999\n",
      "----------\n",
      "train Loss: 0.4593 Acc: 0.7869\n",
      "has spend time 114m 9s/n\n",
      "val Loss: 0.5502 Acc: 0.7059\n",
      "has spend time 114m 10s/n\n",
      "\n",
      "Epoch 3214/9999\n",
      "----------\n",
      "train Loss: 0.5106 Acc: 0.7295\n",
      "has spend time 114m 11s/n\n",
      "val Loss: 0.5462 Acc: 0.7059\n",
      "has spend time 114m 12s/n\n",
      "\n",
      "Epoch 3215/9999\n",
      "----------\n",
      "train Loss: 0.5036 Acc: 0.7418\n",
      "has spend time 114m 13s/n\n",
      "val Loss: 0.5644 Acc: 0.7059\n",
      "has spend time 114m 14s/n\n",
      "\n",
      "Epoch 3216/9999\n",
      "----------\n",
      "train Loss: 0.4993 Acc: 0.7828\n",
      "has spend time 114m 16s/n\n",
      "val Loss: 0.5462 Acc: 0.7124\n",
      "has spend time 114m 16s/n\n",
      "\n",
      "Epoch 3217/9999\n",
      "----------\n",
      "train Loss: 0.4875 Acc: 0.7500\n",
      "has spend time 114m 18s/n\n",
      "val Loss: 0.5669 Acc: 0.7059\n",
      "has spend time 114m 18s/n\n",
      "\n",
      "Epoch 3218/9999\n",
      "----------\n",
      "train Loss: 0.4827 Acc: 0.7623\n",
      "has spend time 114m 20s/n\n",
      "val Loss: 0.5486 Acc: 0.7190\n",
      "has spend time 114m 20s/n\n",
      "\n",
      "Epoch 3219/9999\n",
      "----------\n",
      "train Loss: 0.5019 Acc: 0.7377\n",
      "has spend time 114m 22s/n\n",
      "val Loss: 0.5541 Acc: 0.6993\n",
      "has spend time 114m 22s/n\n",
      "\n",
      "Epoch 3220/9999\n",
      "----------\n",
      "train Loss: 0.4914 Acc: 0.7377\n",
      "has spend time 114m 24s/n\n",
      "val Loss: 0.5550 Acc: 0.6993\n",
      "has spend time 114m 24s/n\n",
      "\n",
      "Epoch 3221/9999\n",
      "----------\n",
      "train Loss: 0.5317 Acc: 0.7295\n",
      "has spend time 114m 26s/n\n",
      "val Loss: 0.5562 Acc: 0.7059\n",
      "has spend time 114m 26s/n\n",
      "\n",
      "Epoch 3222/9999\n",
      "----------\n",
      "train Loss: 0.5115 Acc: 0.7295\n",
      "has spend time 114m 28s/n\n",
      "val Loss: 0.5645 Acc: 0.6993\n",
      "has spend time 114m 29s/n\n",
      "\n",
      "Epoch 3223/9999\n",
      "----------\n",
      "train Loss: 0.4985 Acc: 0.7254\n",
      "has spend time 114m 30s/n\n",
      "val Loss: 0.5518 Acc: 0.7124\n",
      "has spend time 114m 31s/n\n",
      "\n",
      "Epoch 3224/9999\n",
      "----------\n",
      "train Loss: 0.5258 Acc: 0.7254\n",
      "has spend time 114m 33s/n\n",
      "val Loss: 0.5528 Acc: 0.6928\n",
      "has spend time 114m 33s/n\n",
      "\n",
      "Epoch 3225/9999\n",
      "----------\n",
      "train Loss: 0.5109 Acc: 0.7295\n",
      "has spend time 114m 35s/n\n",
      "val Loss: 0.5665 Acc: 0.6993\n",
      "has spend time 114m 35s/n\n",
      "\n",
      "Epoch 3226/9999\n",
      "----------\n",
      "train Loss: 0.5371 Acc: 0.7377\n",
      "has spend time 114m 37s/n\n",
      "val Loss: 0.5471 Acc: 0.7124\n",
      "has spend time 114m 37s/n\n",
      "\n",
      "Epoch 3227/9999\n",
      "----------\n",
      "train Loss: 0.4933 Acc: 0.7869\n",
      "has spend time 114m 39s/n\n",
      "val Loss: 0.5539 Acc: 0.6993\n",
      "has spend time 114m 40s/n\n",
      "\n",
      "Epoch 3228/9999\n",
      "----------\n",
      "train Loss: 0.4997 Acc: 0.7418\n",
      "has spend time 114m 41s/n\n",
      "val Loss: 0.5486 Acc: 0.7255\n",
      "has spend time 114m 42s/n\n",
      "\n",
      "Epoch 3229/9999\n",
      "----------\n",
      "train Loss: 0.4950 Acc: 0.7418\n",
      "has spend time 114m 43s/n\n",
      "val Loss: 0.5538 Acc: 0.7059\n",
      "has spend time 114m 44s/n\n",
      "\n",
      "Epoch 3230/9999\n",
      "----------\n",
      "train Loss: 0.4976 Acc: 0.7664\n",
      "has spend time 114m 45s/n\n",
      "val Loss: 0.5485 Acc: 0.7059\n",
      "has spend time 114m 46s/n\n",
      "\n",
      "Epoch 3231/9999\n",
      "----------\n",
      "train Loss: 0.5086 Acc: 0.7131\n",
      "has spend time 114m 47s/n\n",
      "val Loss: 0.5596 Acc: 0.6993\n",
      "has spend time 114m 48s/n\n",
      "\n",
      "Epoch 3232/9999\n",
      "----------\n",
      "train Loss: 0.5061 Acc: 0.7131\n",
      "has spend time 114m 49s/n\n",
      "val Loss: 0.5512 Acc: 0.7059\n",
      "has spend time 114m 50s/n\n",
      "\n",
      "Epoch 3233/9999\n",
      "----------\n",
      "train Loss: 0.4837 Acc: 0.7500\n",
      "has spend time 114m 52s/n\n",
      "val Loss: 0.5607 Acc: 0.6993\n",
      "has spend time 114m 52s/n\n",
      "\n",
      "Epoch 3234/9999\n",
      "----------\n",
      "train Loss: 0.4997 Acc: 0.7623\n",
      "has spend time 114m 54s/n\n",
      "val Loss: 0.5495 Acc: 0.7124\n",
      "has spend time 114m 55s/n\n",
      "\n",
      "Epoch 3235/9999\n",
      "----------\n",
      "train Loss: 0.5372 Acc: 0.7172\n",
      "has spend time 114m 56s/n\n",
      "val Loss: 0.5628 Acc: 0.6993\n",
      "has spend time 114m 57s/n\n",
      "\n",
      "Epoch 3236/9999\n",
      "----------\n",
      "train Loss: 0.5007 Acc: 0.7213\n",
      "has spend time 114m 58s/n\n",
      "val Loss: 0.5554 Acc: 0.6928\n",
      "has spend time 114m 59s/n\n",
      "\n",
      "Epoch 3237/9999\n",
      "----------\n",
      "train Loss: 0.5461 Acc: 0.7090\n",
      "has spend time 115m 0s/n\n",
      "val Loss: 0.5583 Acc: 0.7059\n",
      "has spend time 115m 1s/n\n",
      "\n",
      "Epoch 3238/9999\n",
      "----------\n",
      "train Loss: 0.5093 Acc: 0.7336\n",
      "has spend time 115m 2s/n\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val Loss: 0.5448 Acc: 0.7190\n",
      "has spend time 115m 3s/n\n",
      "\n",
      "Epoch 3239/9999\n",
      "----------\n",
      "train Loss: 0.5129 Acc: 0.7336\n",
      "has spend time 115m 4s/n\n",
      "val Loss: 0.5482 Acc: 0.7124\n",
      "has spend time 115m 5s/n\n",
      "\n",
      "Epoch 3240/9999\n",
      "----------\n",
      "train Loss: 0.4921 Acc: 0.7705\n",
      "has spend time 115m 6s/n\n",
      "val Loss: 0.5416 Acc: 0.7124\n",
      "has spend time 115m 7s/n\n",
      "\n",
      "Epoch 3241/9999\n",
      "----------\n",
      "train Loss: 0.5123 Acc: 0.7008\n",
      "has spend time 115m 8s/n\n",
      "val Loss: 0.5495 Acc: 0.7059\n",
      "has spend time 115m 9s/n\n",
      "\n",
      "Epoch 3242/9999\n",
      "----------\n",
      "train Loss: 0.5211 Acc: 0.7418\n",
      "has spend time 115m 10s/n\n",
      "val Loss: 0.5532 Acc: 0.6993\n",
      "has spend time 115m 11s/n\n",
      "\n",
      "Epoch 3243/9999\n",
      "----------\n",
      "train Loss: 0.4998 Acc: 0.7500\n",
      "has spend time 115m 12s/n\n",
      "val Loss: 0.5567 Acc: 0.6993\n",
      "has spend time 115m 13s/n\n",
      "\n",
      "Epoch 3244/9999\n",
      "----------\n",
      "train Loss: 0.4993 Acc: 0.7541\n",
      "has spend time 115m 15s/n\n",
      "val Loss: 0.5545 Acc: 0.7059\n",
      "has spend time 115m 15s/n\n",
      "\n",
      "Epoch 3245/9999\n",
      "----------\n",
      "train Loss: 0.4879 Acc: 0.7254\n",
      "has spend time 115m 17s/n\n",
      "val Loss: 0.5542 Acc: 0.7190\n",
      "has spend time 115m 17s/n\n",
      "\n",
      "Epoch 3246/9999\n",
      "----------\n",
      "train Loss: 0.4960 Acc: 0.7746\n",
      "has spend time 115m 19s/n\n",
      "val Loss: 0.5421 Acc: 0.7190\n",
      "has spend time 115m 20s/n\n",
      "\n",
      "Epoch 3247/9999\n",
      "----------\n",
      "train Loss: 0.4990 Acc: 0.7910\n",
      "has spend time 115m 21s/n\n",
      "val Loss: 0.5564 Acc: 0.6993\n",
      "has spend time 115m 22s/n\n",
      "\n",
      "Epoch 3248/9999\n",
      "----------\n",
      "train Loss: 0.4958 Acc: 0.7664\n",
      "has spend time 115m 23s/n\n",
      "val Loss: 0.5620 Acc: 0.6993\n",
      "has spend time 115m 24s/n\n",
      "\n",
      "Epoch 3249/9999\n",
      "----------\n",
      "train Loss: 0.5047 Acc: 0.7254\n",
      "has spend time 115m 25s/n\n",
      "val Loss: 0.5571 Acc: 0.6993\n",
      "has spend time 115m 26s/n\n",
      "\n",
      "Epoch 3250/9999\n",
      "----------\n",
      "train Loss: 0.5067 Acc: 0.7500\n",
      "has spend time 115m 27s/n\n",
      "val Loss: 0.5462 Acc: 0.7190\n",
      "has spend time 115m 28s/n\n",
      "\n",
      "Epoch 3251/9999\n",
      "----------\n",
      "train Loss: 0.5090 Acc: 0.7418\n",
      "has spend time 115m 29s/n\n",
      "val Loss: 0.5463 Acc: 0.7059\n",
      "has spend time 115m 30s/n\n",
      "\n",
      "Epoch 3252/9999\n",
      "----------\n",
      "train Loss: 0.4949 Acc: 0.7459\n",
      "has spend time 115m 32s/n\n",
      "val Loss: 0.5489 Acc: 0.7059\n",
      "has spend time 115m 33s/n\n",
      "\n",
      "Epoch 3253/9999\n",
      "----------\n",
      "train Loss: 0.5099 Acc: 0.7664\n",
      "has spend time 115m 34s/n\n",
      "val Loss: 0.5463 Acc: 0.7124\n",
      "has spend time 115m 35s/n\n",
      "\n",
      "Epoch 3254/9999\n",
      "----------\n",
      "train Loss: 0.4820 Acc: 0.7746\n",
      "has spend time 115m 36s/n\n",
      "val Loss: 0.5404 Acc: 0.7190\n",
      "has spend time 115m 37s/n\n",
      "\n",
      "Epoch 3255/9999\n",
      "----------\n",
      "train Loss: 0.5084 Acc: 0.7623\n",
      "has spend time 115m 38s/n\n",
      "val Loss: 0.5446 Acc: 0.7124\n",
      "has spend time 115m 39s/n\n",
      "\n",
      "Epoch 3256/9999\n",
      "----------\n",
      "train Loss: 0.4901 Acc: 0.7254\n",
      "has spend time 115m 40s/n\n",
      "val Loss: 0.5527 Acc: 0.7059\n",
      "has spend time 115m 41s/n\n",
      "\n",
      "Epoch 3257/9999\n",
      "----------\n",
      "train Loss: 0.5164 Acc: 0.7541\n",
      "has spend time 115m 42s/n\n",
      "val Loss: 0.5645 Acc: 0.6993\n",
      "has spend time 115m 43s/n\n",
      "\n",
      "Epoch 3258/9999\n",
      "----------\n",
      "train Loss: 0.5270 Acc: 0.6926\n",
      "has spend time 115m 44s/n\n",
      "val Loss: 0.5497 Acc: 0.7059\n",
      "has spend time 115m 45s/n\n",
      "\n",
      "Epoch 3259/9999\n",
      "----------\n",
      "train Loss: 0.4607 Acc: 0.7664\n",
      "has spend time 115m 46s/n\n",
      "val Loss: 0.5530 Acc: 0.7059\n",
      "has spend time 115m 47s/n\n",
      "\n",
      "Epoch 3260/9999\n",
      "----------\n",
      "train Loss: 0.5256 Acc: 0.7295\n",
      "has spend time 115m 48s/n\n",
      "val Loss: 0.5454 Acc: 0.7059\n",
      "has spend time 115m 49s/n\n",
      "\n",
      "Epoch 3261/9999\n",
      "----------\n",
      "train Loss: 0.5154 Acc: 0.7172\n",
      "has spend time 115m 51s/n\n",
      "val Loss: 0.5495 Acc: 0.7059\n",
      "has spend time 115m 52s/n\n",
      "\n",
      "Epoch 3262/9999\n",
      "----------\n",
      "train Loss: 0.5070 Acc: 0.7459\n",
      "has spend time 115m 53s/n\n",
      "val Loss: 0.5490 Acc: 0.7059\n",
      "has spend time 115m 54s/n\n",
      "\n",
      "Epoch 3263/9999\n",
      "----------\n",
      "train Loss: 0.5011 Acc: 0.7705\n",
      "has spend time 115m 55s/n\n",
      "val Loss: 0.5643 Acc: 0.7059\n",
      "has spend time 115m 56s/n\n",
      "\n",
      "Epoch 3264/9999\n",
      "----------\n",
      "train Loss: 0.5129 Acc: 0.7131\n",
      "has spend time 115m 58s/n\n",
      "val Loss: 0.5421 Acc: 0.7124\n",
      "has spend time 115m 58s/n\n",
      "\n",
      "Epoch 3265/9999\n",
      "----------\n",
      "train Loss: 0.4996 Acc: 0.7131\n",
      "has spend time 115m 60s/n\n",
      "val Loss: 0.5496 Acc: 0.6993\n",
      "has spend time 116m 0s/n\n",
      "\n",
      "Epoch 3266/9999\n",
      "----------\n",
      "train Loss: 0.4890 Acc: 0.7828\n",
      "has spend time 116m 2s/n\n",
      "val Loss: 0.5500 Acc: 0.7124\n",
      "has spend time 116m 2s/n\n",
      "\n",
      "Epoch 3267/9999\n",
      "----------\n",
      "train Loss: 0.5220 Acc: 0.7541\n",
      "has spend time 116m 4s/n\n",
      "val Loss: 0.5457 Acc: 0.7255\n",
      "has spend time 116m 4s/n\n",
      "\n",
      "Epoch 3268/9999\n",
      "----------\n",
      "train Loss: 0.5055 Acc: 0.7582\n",
      "has spend time 116m 6s/n\n",
      "val Loss: 0.5447 Acc: 0.7059\n",
      "has spend time 116m 6s/n\n",
      "\n",
      "Epoch 3269/9999\n",
      "----------\n",
      "train Loss: 0.5052 Acc: 0.7213\n",
      "has spend time 116m 8s/n\n",
      "val Loss: 0.5392 Acc: 0.7190\n",
      "has spend time 116m 8s/n\n",
      "\n",
      "Epoch 3270/9999\n",
      "----------\n",
      "train Loss: 0.5234 Acc: 0.7377\n",
      "has spend time 116m 10s/n\n",
      "val Loss: 0.5450 Acc: 0.7124\n",
      "has spend time 116m 11s/n\n",
      "\n",
      "Epoch 3271/9999\n",
      "----------\n",
      "train Loss: 0.5171 Acc: 0.7336\n",
      "has spend time 116m 12s/n\n",
      "val Loss: 0.5401 Acc: 0.7059\n",
      "has spend time 116m 13s/n\n",
      "\n",
      "Epoch 3272/9999\n",
      "----------\n",
      "train Loss: 0.4800 Acc: 0.7418\n",
      "has spend time 116m 15s/n\n",
      "val Loss: 0.5465 Acc: 0.7059\n",
      "has spend time 116m 15s/n\n",
      "\n",
      "Epoch 3273/9999\n",
      "----------\n",
      "train Loss: 0.4882 Acc: 0.7828\n",
      "has spend time 116m 17s/n\n",
      "val Loss: 0.5548 Acc: 0.6993\n",
      "has spend time 116m 17s/n\n",
      "\n",
      "Epoch 3274/9999\n",
      "----------\n",
      "train Loss: 0.5330 Acc: 0.7131\n",
      "has spend time 116m 19s/n\n",
      "val Loss: 0.5514 Acc: 0.7059\n",
      "has spend time 116m 19s/n\n",
      "\n",
      "Epoch 3275/9999\n",
      "----------\n",
      "train Loss: 0.5144 Acc: 0.7377\n",
      "has spend time 116m 21s/n\n",
      "val Loss: 0.5422 Acc: 0.7320\n",
      "has spend time 116m 21s/n\n",
      "\n",
      "Epoch 3276/9999\n",
      "----------\n",
      "train Loss: 0.5088 Acc: 0.7418\n",
      "has spend time 116m 23s/n\n",
      "val Loss: 0.5489 Acc: 0.7124\n",
      "has spend time 116m 24s/n\n",
      "\n",
      "Epoch 3277/9999\n",
      "----------\n",
      "train Loss: 0.5151 Acc: 0.7418\n",
      "has spend time 116m 25s/n\n",
      "val Loss: 0.5471 Acc: 0.6993\n",
      "has spend time 116m 26s/n\n",
      "\n",
      "Epoch 3278/9999\n",
      "----------\n",
      "train Loss: 0.5037 Acc: 0.7131\n",
      "has spend time 116m 27s/n\n",
      "val Loss: 0.5568 Acc: 0.6928\n",
      "has spend time 116m 28s/n\n",
      "\n",
      "Epoch 3279/9999\n",
      "----------\n",
      "train Loss: 0.5009 Acc: 0.7623\n",
      "has spend time 116m 29s/n\n",
      "val Loss: 0.5563 Acc: 0.6863\n",
      "has spend time 116m 30s/n\n",
      "\n",
      "Epoch 3280/9999\n",
      "----------\n",
      "train Loss: 0.5071 Acc: 0.7213\n",
      "has spend time 116m 31s/n\n",
      "val Loss: 0.5474 Acc: 0.7190\n",
      "has spend time 116m 32s/n\n",
      "\n",
      "Epoch 3281/9999\n",
      "----------\n",
      "train Loss: 0.5283 Acc: 0.6967\n",
      "has spend time 116m 34s/n\n",
      "val Loss: 0.5443 Acc: 0.7124\n",
      "has spend time 116m 34s/n\n",
      "\n",
      "Epoch 3282/9999\n",
      "----------\n",
      "train Loss: 0.4920 Acc: 0.7623\n",
      "has spend time 116m 36s/n\n",
      "val Loss: 0.5522 Acc: 0.7059\n",
      "has spend time 116m 36s/n\n",
      "\n",
      "Epoch 3283/9999\n",
      "----------\n",
      "train Loss: 0.4934 Acc: 0.7541\n",
      "has spend time 116m 38s/n\n",
      "val Loss: 0.5544 Acc: 0.7059\n",
      "has spend time 116m 38s/n\n",
      "\n",
      "Epoch 3284/9999\n",
      "----------\n",
      "train Loss: 0.5051 Acc: 0.7746\n",
      "has spend time 116m 40s/n\n",
      "val Loss: 0.5516 Acc: 0.7124\n",
      "has spend time 116m 40s/n\n",
      "\n",
      "Epoch 3285/9999\n",
      "----------\n",
      "train Loss: 0.5078 Acc: 0.7336\n",
      "has spend time 116m 42s/n\n",
      "val Loss: 0.5493 Acc: 0.7059\n",
      "has spend time 116m 42s/n\n",
      "\n",
      "Epoch 3286/9999\n",
      "----------\n",
      "train Loss: 0.4857 Acc: 0.7377\n",
      "has spend time 116m 44s/n\n",
      "val Loss: 0.5438 Acc: 0.7059\n",
      "has spend time 116m 44s/n\n",
      "\n",
      "Epoch 3287/9999\n",
      "----------\n",
      "train Loss: 0.5002 Acc: 0.7500\n",
      "has spend time 116m 46s/n\n",
      "val Loss: 0.5444 Acc: 0.7124\n",
      "has spend time 116m 47s/n\n",
      "\n",
      "Epoch 3288/9999\n",
      "----------\n",
      "train Loss: 0.4840 Acc: 0.7295\n",
      "has spend time 116m 48s/n\n",
      "val Loss: 0.5522 Acc: 0.7124\n",
      "has spend time 116m 49s/n\n",
      "\n",
      "Epoch 3289/9999\n",
      "----------\n",
      "train Loss: 0.5129 Acc: 0.7336\n",
      "has spend time 116m 51s/n\n",
      "val Loss: 0.5702 Acc: 0.6928\n",
      "has spend time 116m 51s/n\n",
      "\n",
      "Epoch 3290/9999\n",
      "----------\n",
      "train Loss: 0.4687 Acc: 0.7828\n",
      "has spend time 116m 53s/n\n",
      "val Loss: 0.5572 Acc: 0.6993\n",
      "has spend time 116m 53s/n\n",
      "\n",
      "Epoch 3291/9999\n",
      "----------\n",
      "train Loss: 0.4824 Acc: 0.7500\n",
      "has spend time 116m 55s/n\n",
      "val Loss: 0.5534 Acc: 0.7059\n",
      "has spend time 116m 55s/n\n",
      "\n",
      "Epoch 3292/9999\n",
      "----------\n",
      "train Loss: 0.5045 Acc: 0.7213\n",
      "has spend time 116m 57s/n\n",
      "val Loss: 0.5659 Acc: 0.6993\n",
      "has spend time 116m 57s/n\n",
      "\n",
      "Epoch 3293/9999\n",
      "----------\n",
      "train Loss: 0.5125 Acc: 0.7541\n",
      "has spend time 116m 59s/n\n",
      "val Loss: 0.5479 Acc: 0.7124\n",
      "has spend time 116m 60s/n\n",
      "\n",
      "Epoch 3294/9999\n",
      "----------\n",
      "train Loss: 0.5009 Acc: 0.7172\n",
      "has spend time 117m 1s/n\n",
      "val Loss: 0.5555 Acc: 0.6993\n",
      "has spend time 117m 2s/n\n",
      "\n",
      "Epoch 3295/9999\n",
      "----------\n",
      "train Loss: 0.5189 Acc: 0.7336\n",
      "has spend time 117m 3s/n\n",
      "val Loss: 0.5553 Acc: 0.6928\n",
      "has spend time 117m 4s/n\n",
      "\n",
      "Epoch 3296/9999\n",
      "----------\n",
      "train Loss: 0.5002 Acc: 0.7377\n",
      "has spend time 117m 6s/n\n",
      "val Loss: 0.5526 Acc: 0.6993\n",
      "has spend time 117m 6s/n\n",
      "\n",
      "Epoch 3297/9999\n",
      "----------\n",
      "train Loss: 0.5190 Acc: 0.7582\n",
      "has spend time 117m 8s/n\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val Loss: 0.5547 Acc: 0.7059\n",
      "has spend time 117m 8s/n\n",
      "\n",
      "Epoch 3298/9999\n",
      "----------\n",
      "train Loss: 0.5365 Acc: 0.7049\n",
      "has spend time 117m 10s/n\n",
      "val Loss: 0.5464 Acc: 0.7124\n",
      "has spend time 117m 10s/n\n",
      "\n",
      "Epoch 3299/9999\n",
      "----------\n",
      "train Loss: 0.5115 Acc: 0.7418\n",
      "has spend time 117m 12s/n\n",
      "val Loss: 0.5495 Acc: 0.7190\n",
      "has spend time 117m 12s/n\n",
      "\n",
      "Epoch 3300/9999\n",
      "----------\n",
      "train Loss: 0.5105 Acc: 0.7500\n",
      "has spend time 117m 14s/n\n",
      "val Loss: 0.5518 Acc: 0.7255\n",
      "has spend time 117m 15s/n\n",
      "\n",
      "Epoch 3301/9999\n",
      "----------\n",
      "train Loss: 0.5121 Acc: 0.7172\n",
      "has spend time 117m 16s/n\n",
      "val Loss: 0.5457 Acc: 0.7190\n",
      "has spend time 117m 17s/n\n",
      "\n",
      "Epoch 3302/9999\n",
      "----------\n",
      "train Loss: 0.5175 Acc: 0.7500\n",
      "has spend time 117m 19s/n\n",
      "val Loss: 0.5629 Acc: 0.6993\n",
      "has spend time 117m 19s/n\n",
      "\n",
      "Epoch 3303/9999\n",
      "----------\n",
      "train Loss: 0.5126 Acc: 0.7418\n",
      "has spend time 117m 21s/n\n",
      "val Loss: 0.5512 Acc: 0.7124\n",
      "has spend time 117m 22s/n\n",
      "\n",
      "Epoch 3304/9999\n",
      "----------\n",
      "train Loss: 0.5080 Acc: 0.7418\n",
      "has spend time 117m 23s/n\n",
      "val Loss: 0.5579 Acc: 0.7059\n",
      "has spend time 117m 24s/n\n",
      "\n",
      "Epoch 3305/9999\n",
      "----------\n",
      "train Loss: 0.5064 Acc: 0.7623\n",
      "has spend time 117m 25s/n\n",
      "val Loss: 0.5486 Acc: 0.7059\n",
      "has spend time 117m 26s/n\n",
      "\n",
      "Epoch 3306/9999\n",
      "----------\n",
      "train Loss: 0.4815 Acc: 0.7541\n",
      "has spend time 117m 27s/n\n",
      "val Loss: 0.5532 Acc: 0.7059\n",
      "has spend time 117m 28s/n\n",
      "\n",
      "Epoch 3307/9999\n",
      "----------\n",
      "train Loss: 0.5200 Acc: 0.7295\n",
      "has spend time 117m 29s/n\n",
      "val Loss: 0.5595 Acc: 0.6863\n",
      "has spend time 117m 30s/n\n",
      "\n",
      "Epoch 3308/9999\n",
      "----------\n",
      "train Loss: 0.5122 Acc: 0.7418\n",
      "has spend time 117m 31s/n\n",
      "val Loss: 0.5461 Acc: 0.7124\n",
      "has spend time 117m 32s/n\n",
      "\n",
      "Epoch 3309/9999\n",
      "----------\n",
      "train Loss: 0.4935 Acc: 0.7664\n",
      "has spend time 117m 33s/n\n",
      "val Loss: 0.5514 Acc: 0.7190\n",
      "has spend time 117m 34s/n\n",
      "\n",
      "Epoch 3310/9999\n",
      "----------\n",
      "train Loss: 0.4950 Acc: 0.7541\n",
      "has spend time 117m 35s/n\n",
      "val Loss: 0.5426 Acc: 0.7190\n",
      "has spend time 117m 36s/n\n",
      "\n",
      "Epoch 3311/9999\n",
      "----------\n",
      "train Loss: 0.5220 Acc: 0.7295\n",
      "has spend time 117m 37s/n\n",
      "val Loss: 0.5433 Acc: 0.7190\n",
      "has spend time 117m 38s/n\n",
      "\n",
      "Epoch 3312/9999\n",
      "----------\n",
      "train Loss: 0.4953 Acc: 0.7377\n",
      "has spend time 117m 39s/n\n",
      "val Loss: 0.5449 Acc: 0.7255\n",
      "has spend time 117m 40s/n\n",
      "\n",
      "Epoch 3313/9999\n",
      "----------\n",
      "train Loss: 0.5056 Acc: 0.7172\n",
      "has spend time 117m 42s/n\n",
      "val Loss: 0.5488 Acc: 0.7190\n",
      "has spend time 117m 42s/n\n",
      "\n",
      "Epoch 3314/9999\n",
      "----------\n",
      "train Loss: 0.5161 Acc: 0.7336\n",
      "has spend time 117m 44s/n\n",
      "val Loss: 0.5556 Acc: 0.6993\n",
      "has spend time 117m 45s/n\n",
      "\n",
      "Epoch 3315/9999\n",
      "----------\n",
      "train Loss: 0.5067 Acc: 0.7500\n",
      "has spend time 117m 46s/n\n",
      "val Loss: 0.5540 Acc: 0.7059\n",
      "has spend time 117m 47s/n\n",
      "\n",
      "Epoch 3316/9999\n",
      "----------\n",
      "train Loss: 0.4854 Acc: 0.7910\n",
      "has spend time 117m 48s/n\n",
      "val Loss: 0.5451 Acc: 0.7124\n",
      "has spend time 117m 49s/n\n",
      "\n",
      "Epoch 3317/9999\n",
      "----------\n",
      "train Loss: 0.5091 Acc: 0.7295\n",
      "has spend time 117m 50s/n\n",
      "val Loss: 0.5516 Acc: 0.6993\n",
      "has spend time 117m 51s/n\n",
      "\n",
      "Epoch 3318/9999\n",
      "----------\n",
      "train Loss: 0.5289 Acc: 0.7090\n",
      "has spend time 117m 52s/n\n",
      "val Loss: 0.5498 Acc: 0.7124\n",
      "has spend time 117m 53s/n\n",
      "\n",
      "Epoch 3319/9999\n",
      "----------\n",
      "train Loss: 0.4927 Acc: 0.7541\n",
      "has spend time 117m 54s/n\n",
      "val Loss: 0.5596 Acc: 0.7059\n",
      "has spend time 117m 55s/n\n",
      "\n",
      "Epoch 3320/9999\n",
      "----------\n",
      "train Loss: 0.4983 Acc: 0.7377\n",
      "has spend time 117m 57s/n\n",
      "val Loss: 0.5575 Acc: 0.6993\n",
      "has spend time 117m 57s/n\n",
      "\n",
      "Epoch 3321/9999\n",
      "----------\n",
      "train Loss: 0.5007 Acc: 0.7418\n",
      "has spend time 117m 59s/n\n",
      "val Loss: 0.5419 Acc: 0.7124\n",
      "has spend time 117m 60s/n\n",
      "\n",
      "Epoch 3322/9999\n",
      "----------\n",
      "train Loss: 0.5166 Acc: 0.7213\n",
      "has spend time 118m 1s/n\n",
      "val Loss: 0.5514 Acc: 0.7059\n",
      "has spend time 118m 2s/n\n",
      "\n",
      "Epoch 3323/9999\n",
      "----------\n",
      "train Loss: 0.5740 Acc: 0.7008\n",
      "has spend time 118m 3s/n\n",
      "val Loss: 0.5602 Acc: 0.6993\n",
      "has spend time 118m 4s/n\n",
      "\n",
      "Epoch 3324/9999\n",
      "----------\n",
      "train Loss: 0.4964 Acc: 0.7705\n",
      "has spend time 118m 5s/n\n",
      "val Loss: 0.5819 Acc: 0.6993\n",
      "has spend time 118m 6s/n\n",
      "\n",
      "Epoch 3325/9999\n",
      "----------\n",
      "train Loss: 0.5107 Acc: 0.7172\n",
      "has spend time 118m 7s/n\n",
      "val Loss: 0.5487 Acc: 0.7059\n",
      "has spend time 118m 8s/n\n",
      "\n",
      "Epoch 3326/9999\n",
      "----------\n",
      "train Loss: 0.5105 Acc: 0.7582\n",
      "has spend time 118m 9s/n\n",
      "val Loss: 0.5684 Acc: 0.6863\n",
      "has spend time 118m 10s/n\n",
      "\n",
      "Epoch 3327/9999\n",
      "----------\n",
      "train Loss: 0.5069 Acc: 0.7459\n",
      "has spend time 118m 11s/n\n",
      "val Loss: 0.5551 Acc: 0.7124\n",
      "has spend time 118m 12s/n\n",
      "\n",
      "Epoch 3328/9999\n",
      "----------\n",
      "train Loss: 0.5183 Acc: 0.7377\n",
      "has spend time 118m 14s/n\n",
      "val Loss: 0.5458 Acc: 0.7124\n",
      "has spend time 118m 14s/n\n",
      "\n",
      "Epoch 3329/9999\n",
      "----------\n",
      "train Loss: 0.5236 Acc: 0.7213\n",
      "has spend time 118m 16s/n\n",
      "val Loss: 0.5587 Acc: 0.7190\n",
      "has spend time 118m 16s/n\n",
      "\n",
      "Epoch 3330/9999\n",
      "----------\n",
      "train Loss: 0.4986 Acc: 0.7541\n",
      "has spend time 118m 18s/n\n",
      "val Loss: 0.5605 Acc: 0.7059\n",
      "has spend time 118m 18s/n\n",
      "\n",
      "Epoch 3331/9999\n",
      "----------\n",
      "train Loss: 0.5075 Acc: 0.7172\n",
      "has spend time 118m 20s/n\n",
      "val Loss: 0.5554 Acc: 0.6993\n",
      "has spend time 118m 20s/n\n",
      "\n",
      "Epoch 3332/9999\n",
      "----------\n",
      "train Loss: 0.5385 Acc: 0.7090\n",
      "has spend time 118m 22s/n\n",
      "val Loss: 0.5416 Acc: 0.7255\n",
      "has spend time 118m 23s/n\n",
      "\n",
      "Epoch 3333/9999\n",
      "----------\n",
      "train Loss: 0.5550 Acc: 0.7131\n",
      "has spend time 118m 24s/n\n",
      "val Loss: 0.5566 Acc: 0.7059\n",
      "has spend time 118m 25s/n\n",
      "\n",
      "Epoch 3334/9999\n",
      "----------\n",
      "train Loss: 0.4907 Acc: 0.7459\n",
      "has spend time 118m 27s/n\n",
      "val Loss: 0.5391 Acc: 0.7190\n",
      "has spend time 118m 27s/n\n",
      "\n",
      "Epoch 3335/9999\n",
      "----------\n",
      "train Loss: 0.4947 Acc: 0.7254\n",
      "has spend time 118m 29s/n\n",
      "val Loss: 0.5404 Acc: 0.7255\n",
      "has spend time 118m 29s/n\n",
      "\n",
      "Epoch 3336/9999\n",
      "----------\n",
      "train Loss: 0.5176 Acc: 0.7172\n",
      "has spend time 118m 31s/n\n",
      "val Loss: 0.5460 Acc: 0.6993\n",
      "has spend time 118m 31s/n\n",
      "\n",
      "Epoch 3337/9999\n",
      "----------\n",
      "train Loss: 0.5312 Acc: 0.7172\n",
      "has spend time 118m 33s/n\n",
      "val Loss: 0.5489 Acc: 0.6993\n",
      "has spend time 118m 33s/n\n",
      "\n",
      "Epoch 3338/9999\n",
      "----------\n",
      "train Loss: 0.4867 Acc: 0.7459\n",
      "has spend time 118m 35s/n\n",
      "val Loss: 0.5528 Acc: 0.7124\n",
      "has spend time 118m 36s/n\n",
      "\n",
      "Epoch 3339/9999\n",
      "----------\n",
      "train Loss: 0.5351 Acc: 0.7049\n",
      "has spend time 118m 37s/n\n",
      "val Loss: 0.5546 Acc: 0.6863\n",
      "has spend time 118m 38s/n\n",
      "\n",
      "Epoch 3340/9999\n",
      "----------\n",
      "train Loss: 0.4971 Acc: 0.7664\n",
      "has spend time 118m 39s/n\n",
      "val Loss: 0.5595 Acc: 0.6928\n",
      "has spend time 118m 40s/n\n",
      "\n",
      "Epoch 3341/9999\n",
      "----------\n",
      "train Loss: 0.5081 Acc: 0.7500\n",
      "has spend time 118m 41s/n\n",
      "val Loss: 0.5572 Acc: 0.7059\n",
      "has spend time 118m 42s/n\n",
      "\n",
      "Epoch 3342/9999\n",
      "----------\n",
      "train Loss: 0.5396 Acc: 0.7049\n",
      "has spend time 118m 43s/n\n",
      "val Loss: 0.5376 Acc: 0.7255\n",
      "has spend time 118m 44s/n\n",
      "\n",
      "Epoch 3343/9999\n",
      "----------\n",
      "train Loss: 0.5738 Acc: 0.7008\n",
      "has spend time 118m 45s/n\n",
      "val Loss: 0.5522 Acc: 0.6928\n",
      "has spend time 118m 46s/n\n",
      "\n",
      "Epoch 3344/9999\n",
      "----------\n",
      "train Loss: 0.5059 Acc: 0.7295\n",
      "has spend time 118m 48s/n\n",
      "val Loss: 0.5493 Acc: 0.7124\n",
      "has spend time 118m 48s/n\n",
      "\n",
      "Epoch 3345/9999\n",
      "----------\n",
      "train Loss: 0.4852 Acc: 0.7623\n",
      "has spend time 118m 50s/n\n",
      "val Loss: 0.5582 Acc: 0.6993\n",
      "has spend time 118m 50s/n\n",
      "\n",
      "Epoch 3346/9999\n",
      "----------\n",
      "train Loss: 0.4735 Acc: 0.7828\n",
      "has spend time 118m 52s/n\n",
      "val Loss: 0.5459 Acc: 0.7059\n",
      "has spend time 118m 53s/n\n",
      "\n",
      "Epoch 3347/9999\n",
      "----------\n",
      "train Loss: 0.5253 Acc: 0.7090\n",
      "has spend time 118m 54s/n\n",
      "val Loss: 0.5411 Acc: 0.7124\n",
      "has spend time 118m 55s/n\n",
      "\n",
      "Epoch 3348/9999\n",
      "----------\n",
      "train Loss: 0.4948 Acc: 0.7500\n",
      "has spend time 118m 56s/n\n",
      "val Loss: 0.5504 Acc: 0.7190\n",
      "has spend time 118m 57s/n\n",
      "\n",
      "Epoch 3349/9999\n",
      "----------\n",
      "train Loss: 0.5054 Acc: 0.7664\n",
      "has spend time 118m 58s/n\n",
      "val Loss: 0.5511 Acc: 0.6993\n",
      "has spend time 118m 59s/n\n",
      "\n",
      "Epoch 3350/9999\n",
      "----------\n",
      "train Loss: 0.5317 Acc: 0.7213\n",
      "has spend time 119m 0s/n\n",
      "val Loss: 0.5577 Acc: 0.7059\n",
      "has spend time 119m 1s/n\n",
      "\n",
      "Epoch 3351/9999\n",
      "----------\n",
      "train Loss: 0.5131 Acc: 0.7090\n",
      "has spend time 119m 3s/n\n",
      "val Loss: 0.5492 Acc: 0.7059\n",
      "has spend time 119m 3s/n\n",
      "\n",
      "Epoch 3352/9999\n",
      "----------\n",
      "train Loss: 0.4705 Acc: 0.7705\n",
      "has spend time 119m 5s/n\n",
      "val Loss: 0.5466 Acc: 0.6993\n",
      "has spend time 119m 6s/n\n",
      "\n",
      "Epoch 3353/9999\n",
      "----------\n",
      "train Loss: 0.4886 Acc: 0.7705\n",
      "has spend time 119m 7s/n\n",
      "val Loss: 0.5437 Acc: 0.7124\n",
      "has spend time 119m 8s/n\n",
      "\n",
      "Epoch 3354/9999\n",
      "----------\n",
      "train Loss: 0.5135 Acc: 0.7254\n",
      "has spend time 119m 9s/n\n",
      "val Loss: 0.5519 Acc: 0.7124\n",
      "has spend time 119m 10s/n\n",
      "\n",
      "Epoch 3355/9999\n",
      "----------\n",
      "train Loss: 0.5111 Acc: 0.7582\n",
      "has spend time 119m 12s/n\n",
      "val Loss: 0.5490 Acc: 0.7124\n",
      "has spend time 119m 12s/n\n",
      "\n",
      "Epoch 3356/9999\n",
      "----------\n",
      "train Loss: 0.4898 Acc: 0.7582\n",
      "has spend time 119m 14s/n\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val Loss: 0.5571 Acc: 0.7059\n",
      "has spend time 119m 15s/n\n",
      "\n",
      "Epoch 3357/9999\n",
      "----------\n",
      "train Loss: 0.5450 Acc: 0.7008\n",
      "has spend time 119m 16s/n\n",
      "val Loss: 0.5534 Acc: 0.7124\n",
      "has spend time 119m 17s/n\n",
      "\n",
      "Epoch 3358/9999\n",
      "----------\n",
      "train Loss: 0.5202 Acc: 0.7377\n",
      "has spend time 119m 18s/n\n",
      "val Loss: 0.5504 Acc: 0.6993\n",
      "has spend time 119m 19s/n\n",
      "\n",
      "Epoch 3359/9999\n",
      "----------\n",
      "train Loss: 0.5265 Acc: 0.7500\n",
      "has spend time 119m 20s/n\n",
      "val Loss: 0.5441 Acc: 0.7190\n",
      "has spend time 119m 21s/n\n",
      "\n",
      "Epoch 3360/9999\n",
      "----------\n",
      "train Loss: 0.5246 Acc: 0.7213\n",
      "has spend time 119m 22s/n\n",
      "val Loss: 0.5495 Acc: 0.7059\n",
      "has spend time 119m 23s/n\n",
      "\n",
      "Epoch 3361/9999\n",
      "----------\n",
      "train Loss: 0.4987 Acc: 0.7131\n",
      "has spend time 119m 24s/n\n",
      "val Loss: 0.5551 Acc: 0.6928\n",
      "has spend time 119m 25s/n\n",
      "\n",
      "Epoch 3362/9999\n",
      "----------\n",
      "train Loss: 0.4924 Acc: 0.7131\n",
      "has spend time 119m 26s/n\n",
      "val Loss: 0.5450 Acc: 0.7124\n",
      "has spend time 119m 27s/n\n",
      "\n",
      "Epoch 3363/9999\n",
      "----------\n",
      "train Loss: 0.5243 Acc: 0.7377\n",
      "has spend time 119m 28s/n\n",
      "val Loss: 0.5466 Acc: 0.7190\n",
      "has spend time 119m 29s/n\n",
      "\n",
      "Epoch 3364/9999\n",
      "----------\n",
      "train Loss: 0.4958 Acc: 0.7500\n",
      "has spend time 119m 31s/n\n",
      "val Loss: 0.5417 Acc: 0.7059\n",
      "has spend time 119m 31s/n\n",
      "\n",
      "Epoch 3365/9999\n",
      "----------\n",
      "train Loss: 0.5186 Acc: 0.7336\n",
      "has spend time 119m 33s/n\n",
      "val Loss: 0.5456 Acc: 0.7059\n",
      "has spend time 119m 34s/n\n",
      "\n",
      "Epoch 3366/9999\n",
      "----------\n",
      "train Loss: 0.4938 Acc: 0.7418\n",
      "has spend time 119m 35s/n\n",
      "val Loss: 0.5481 Acc: 0.7124\n",
      "has spend time 119m 36s/n\n",
      "\n",
      "Epoch 3367/9999\n",
      "----------\n",
      "train Loss: 0.4970 Acc: 0.7582\n",
      "has spend time 119m 37s/n\n",
      "val Loss: 0.5620 Acc: 0.6863\n",
      "has spend time 119m 38s/n\n",
      "\n",
      "Epoch 3368/9999\n",
      "----------\n",
      "train Loss: 0.5229 Acc: 0.7213\n",
      "has spend time 119m 39s/n\n",
      "val Loss: 0.5501 Acc: 0.7059\n",
      "has spend time 119m 40s/n\n",
      "\n",
      "Epoch 3369/9999\n",
      "----------\n",
      "train Loss: 0.4990 Acc: 0.7541\n",
      "has spend time 119m 41s/n\n",
      "val Loss: 0.5505 Acc: 0.7190\n",
      "has spend time 119m 42s/n\n",
      "\n",
      "Epoch 3370/9999\n",
      "----------\n",
      "train Loss: 0.5238 Acc: 0.7336\n",
      "has spend time 119m 43s/n\n",
      "val Loss: 0.5523 Acc: 0.6993\n",
      "has spend time 119m 44s/n\n",
      "\n",
      "Epoch 3371/9999\n",
      "----------\n",
      "train Loss: 0.4878 Acc: 0.7705\n",
      "has spend time 119m 46s/n\n",
      "val Loss: 0.5751 Acc: 0.6928\n",
      "has spend time 119m 46s/n\n",
      "\n",
      "Epoch 3372/9999\n",
      "----------\n",
      "train Loss: 0.5144 Acc: 0.7582\n",
      "has spend time 119m 48s/n\n",
      "val Loss: 0.5464 Acc: 0.7124\n",
      "has spend time 119m 49s/n\n",
      "\n",
      "Epoch 3373/9999\n",
      "----------\n",
      "train Loss: 0.5464 Acc: 0.7090\n",
      "has spend time 119m 50s/n\n",
      "val Loss: 0.5418 Acc: 0.7124\n",
      "has spend time 119m 51s/n\n",
      "\n",
      "Epoch 3374/9999\n",
      "----------\n",
      "train Loss: 0.5228 Acc: 0.6967\n",
      "has spend time 119m 52s/n\n",
      "val Loss: 0.5516 Acc: 0.7059\n",
      "has spend time 119m 53s/n\n",
      "\n",
      "Epoch 3375/9999\n",
      "----------\n",
      "train Loss: 0.5167 Acc: 0.7295\n",
      "has spend time 119m 54s/n\n",
      "val Loss: 0.5601 Acc: 0.6993\n",
      "has spend time 119m 55s/n\n",
      "\n",
      "Epoch 3376/9999\n",
      "----------\n",
      "train Loss: 0.4955 Acc: 0.7213\n",
      "has spend time 119m 56s/n\n",
      "val Loss: 0.5531 Acc: 0.6993\n",
      "has spend time 119m 57s/n\n",
      "\n",
      "Epoch 3377/9999\n",
      "----------\n",
      "train Loss: 0.5220 Acc: 0.7090\n",
      "has spend time 119m 58s/n\n",
      "val Loss: 0.5577 Acc: 0.6928\n",
      "has spend time 119m 59s/n\n",
      "\n",
      "Epoch 3378/9999\n",
      "----------\n",
      "train Loss: 0.5273 Acc: 0.7090\n",
      "has spend time 120m 0s/n\n",
      "val Loss: 0.5632 Acc: 0.6928\n",
      "has spend time 120m 1s/n\n",
      "\n",
      "Epoch 3379/9999\n",
      "----------\n",
      "train Loss: 0.5028 Acc: 0.7623\n",
      "has spend time 120m 3s/n\n",
      "val Loss: 0.5544 Acc: 0.7059\n",
      "has spend time 120m 3s/n\n",
      "\n",
      "Epoch 3380/9999\n",
      "----------\n",
      "train Loss: 0.4842 Acc: 0.7664\n",
      "has spend time 120m 5s/n\n",
      "val Loss: 0.5595 Acc: 0.6993\n",
      "has spend time 120m 5s/n\n",
      "\n",
      "Epoch 3381/9999\n",
      "----------\n",
      "train Loss: 0.5118 Acc: 0.7336\n",
      "has spend time 120m 7s/n\n",
      "val Loss: 0.5473 Acc: 0.7190\n",
      "has spend time 120m 7s/n\n",
      "\n",
      "Epoch 3382/9999\n",
      "----------\n",
      "train Loss: 0.4962 Acc: 0.7582\n",
      "has spend time 120m 9s/n\n",
      "val Loss: 0.5465 Acc: 0.7255\n",
      "has spend time 120m 9s/n\n",
      "\n",
      "Epoch 3383/9999\n",
      "----------\n",
      "train Loss: 0.5296 Acc: 0.6967\n",
      "has spend time 120m 11s/n\n",
      "val Loss: 0.5408 Acc: 0.7190\n",
      "has spend time 120m 12s/n\n",
      "\n",
      "Epoch 3384/9999\n",
      "----------\n",
      "train Loss: 0.5117 Acc: 0.7172\n",
      "has spend time 120m 13s/n\n",
      "val Loss: 0.5471 Acc: 0.7255\n",
      "has spend time 120m 14s/n\n",
      "\n",
      "Epoch 3385/9999\n",
      "----------\n",
      "train Loss: 0.5106 Acc: 0.7254\n",
      "has spend time 120m 16s/n\n",
      "val Loss: 0.5435 Acc: 0.7124\n",
      "has spend time 120m 16s/n\n",
      "\n",
      "Epoch 3386/9999\n",
      "----------\n",
      "train Loss: 0.5021 Acc: 0.7213\n",
      "has spend time 120m 18s/n\n",
      "val Loss: 0.5523 Acc: 0.7124\n",
      "has spend time 120m 18s/n\n",
      "\n",
      "Epoch 3387/9999\n",
      "----------\n",
      "train Loss: 0.5230 Acc: 0.7336\n",
      "has spend time 120m 20s/n\n",
      "val Loss: 0.5487 Acc: 0.6993\n",
      "has spend time 120m 20s/n\n",
      "\n",
      "Epoch 3388/9999\n",
      "----------\n",
      "train Loss: 0.5137 Acc: 0.7295\n",
      "has spend time 120m 22s/n\n",
      "val Loss: 0.5425 Acc: 0.7124\n",
      "has spend time 120m 22s/n\n",
      "\n",
      "Epoch 3389/9999\n",
      "----------\n",
      "train Loss: 0.4809 Acc: 0.7582\n",
      "has spend time 120m 24s/n\n",
      "val Loss: 0.5441 Acc: 0.7255\n",
      "has spend time 120m 25s/n\n",
      "\n",
      "Epoch 3390/9999\n",
      "----------\n",
      "train Loss: 0.4975 Acc: 0.7582\n",
      "has spend time 120m 26s/n\n",
      "val Loss: 0.5471 Acc: 0.6993\n",
      "has spend time 120m 27s/n\n",
      "\n",
      "Epoch 3391/9999\n",
      "----------\n",
      "train Loss: 0.5170 Acc: 0.7500\n",
      "has spend time 120m 28s/n\n",
      "val Loss: 0.5507 Acc: 0.7124\n",
      "has spend time 120m 29s/n\n",
      "\n",
      "Epoch 3392/9999\n",
      "----------\n",
      "train Loss: 0.4949 Acc: 0.7664\n",
      "has spend time 120m 31s/n\n",
      "val Loss: 0.5485 Acc: 0.7124\n",
      "has spend time 120m 31s/n\n",
      "\n",
      "Epoch 3393/9999\n",
      "----------\n",
      "train Loss: 0.4951 Acc: 0.7377\n",
      "has spend time 120m 33s/n\n",
      "val Loss: 0.5517 Acc: 0.7190\n",
      "has spend time 120m 33s/n\n",
      "\n",
      "Epoch 3394/9999\n",
      "----------\n",
      "train Loss: 0.4902 Acc: 0.7459\n",
      "has spend time 120m 35s/n\n",
      "val Loss: 0.5462 Acc: 0.7190\n",
      "has spend time 120m 35s/n\n",
      "\n",
      "Epoch 3395/9999\n",
      "----------\n",
      "train Loss: 0.5233 Acc: 0.7377\n",
      "has spend time 120m 37s/n\n",
      "val Loss: 0.5486 Acc: 0.7255\n",
      "has spend time 120m 37s/n\n",
      "\n",
      "Epoch 3396/9999\n",
      "----------\n",
      "train Loss: 0.4902 Acc: 0.7459\n",
      "has spend time 120m 39s/n\n",
      "val Loss: 0.5487 Acc: 0.7059\n",
      "has spend time 120m 39s/n\n",
      "\n",
      "Epoch 3397/9999\n",
      "----------\n",
      "train Loss: 0.4747 Acc: 0.7254\n",
      "has spend time 120m 41s/n\n",
      "val Loss: 0.5416 Acc: 0.7124\n",
      "has spend time 120m 42s/n\n",
      "\n",
      "Epoch 3398/9999\n",
      "----------\n",
      "train Loss: 0.5464 Acc: 0.7049\n",
      "has spend time 120m 43s/n\n",
      "val Loss: 0.5614 Acc: 0.6993\n",
      "has spend time 120m 44s/n\n",
      "\n",
      "Epoch 3399/9999\n",
      "----------\n",
      "train Loss: 0.4926 Acc: 0.7869\n",
      "has spend time 120m 45s/n\n",
      "val Loss: 0.5550 Acc: 0.6993\n",
      "has spend time 120m 46s/n\n",
      "\n",
      "Epoch 3400/9999\n",
      "----------\n",
      "train Loss: 0.5122 Acc: 0.7582\n",
      "has spend time 120m 47s/n\n",
      "val Loss: 0.5603 Acc: 0.7124\n",
      "has spend time 120m 48s/n\n",
      "\n",
      "Epoch 3401/9999\n",
      "----------\n",
      "train Loss: 0.5171 Acc: 0.7213\n",
      "has spend time 120m 49s/n\n",
      "val Loss: 0.5436 Acc: 0.7124\n",
      "has spend time 120m 50s/n\n",
      "\n",
      "Epoch 3402/9999\n",
      "----------\n",
      "train Loss: 0.5162 Acc: 0.7377\n",
      "has spend time 120m 51s/n\n",
      "val Loss: 0.5412 Acc: 0.7124\n",
      "has spend time 120m 52s/n\n",
      "\n",
      "Epoch 3403/9999\n",
      "----------\n",
      "train Loss: 0.5103 Acc: 0.7172\n",
      "has spend time 120m 53s/n\n",
      "val Loss: 0.5585 Acc: 0.7059\n",
      "has spend time 120m 54s/n\n",
      "\n",
      "Epoch 3404/9999\n",
      "----------\n",
      "train Loss: 0.4692 Acc: 0.7869\n",
      "has spend time 120m 55s/n\n",
      "val Loss: 0.5477 Acc: 0.7124\n",
      "has spend time 120m 56s/n\n",
      "\n",
      "Epoch 3405/9999\n",
      "----------\n",
      "train Loss: 0.5203 Acc: 0.7582\n",
      "has spend time 120m 58s/n\n",
      "val Loss: 0.5473 Acc: 0.7124\n",
      "has spend time 120m 58s/n\n",
      "\n",
      "Epoch 3406/9999\n",
      "----------\n",
      "train Loss: 0.5105 Acc: 0.7213\n",
      "has spend time 121m 0s/n\n",
      "val Loss: 0.5418 Acc: 0.7190\n",
      "has spend time 121m 1s/n\n",
      "\n",
      "Epoch 3407/9999\n",
      "----------\n",
      "train Loss: 0.5091 Acc: 0.7664\n",
      "has spend time 121m 2s/n\n",
      "val Loss: 0.5533 Acc: 0.7059\n",
      "has spend time 121m 3s/n\n",
      "\n",
      "Epoch 3408/9999\n",
      "----------\n",
      "train Loss: 0.4910 Acc: 0.7541\n",
      "has spend time 121m 4s/n\n",
      "val Loss: 0.5577 Acc: 0.6928\n",
      "has spend time 121m 5s/n\n",
      "\n",
      "Epoch 3409/9999\n",
      "----------\n",
      "train Loss: 0.5211 Acc: 0.7295\n",
      "has spend time 121m 6s/n\n",
      "val Loss: 0.5478 Acc: 0.6993\n",
      "has spend time 121m 7s/n\n",
      "\n",
      "Epoch 3410/9999\n",
      "----------\n",
      "train Loss: 0.5271 Acc: 0.7377\n",
      "has spend time 121m 8s/n\n",
      "val Loss: 0.5489 Acc: 0.7059\n",
      "has spend time 121m 9s/n\n",
      "\n",
      "Epoch 3411/9999\n",
      "----------\n",
      "train Loss: 0.5096 Acc: 0.7377\n",
      "has spend time 121m 10s/n\n",
      "val Loss: 0.5430 Acc: 0.7059\n",
      "has spend time 121m 11s/n\n",
      "\n",
      "Epoch 3412/9999\n",
      "----------\n",
      "train Loss: 0.4948 Acc: 0.7172\n",
      "has spend time 121m 13s/n\n",
      "val Loss: 0.5453 Acc: 0.7190\n",
      "has spend time 121m 13s/n\n",
      "\n",
      "Epoch 3413/9999\n",
      "----------\n",
      "train Loss: 0.5083 Acc: 0.7295\n",
      "has spend time 121m 15s/n\n",
      "val Loss: 0.5523 Acc: 0.7190\n",
      "has spend time 121m 15s/n\n",
      "\n",
      "Epoch 3414/9999\n",
      "----------\n",
      "train Loss: 0.5270 Acc: 0.7295\n",
      "has spend time 121m 17s/n\n",
      "val Loss: 0.5456 Acc: 0.7320\n",
      "has spend time 121m 17s/n\n",
      "\n",
      "Epoch 3415/9999\n",
      "----------\n",
      "train Loss: 0.4780 Acc: 0.7746\n",
      "has spend time 121m 19s/n\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val Loss: 0.5509 Acc: 0.7190\n",
      "has spend time 121m 19s/n\n",
      "\n",
      "Epoch 3416/9999\n",
      "----------\n",
      "train Loss: 0.4936 Acc: 0.7418\n",
      "has spend time 121m 21s/n\n",
      "val Loss: 0.5484 Acc: 0.7190\n",
      "has spend time 121m 22s/n\n",
      "\n",
      "Epoch 3417/9999\n",
      "----------\n",
      "train Loss: 0.5032 Acc: 0.7336\n",
      "has spend time 121m 23s/n\n",
      "val Loss: 0.5446 Acc: 0.7059\n",
      "has spend time 121m 24s/n\n",
      "\n",
      "Epoch 3418/9999\n",
      "----------\n",
      "train Loss: 0.5435 Acc: 0.7131\n",
      "has spend time 121m 26s/n\n",
      "val Loss: 0.5494 Acc: 0.7059\n",
      "has spend time 121m 26s/n\n",
      "\n",
      "Epoch 3419/9999\n",
      "----------\n",
      "train Loss: 0.5247 Acc: 0.7295\n",
      "has spend time 121m 28s/n\n",
      "val Loss: 0.5502 Acc: 0.7255\n",
      "has spend time 121m 28s/n\n",
      "\n",
      "Epoch 3420/9999\n",
      "----------\n",
      "train Loss: 0.5277 Acc: 0.7377\n",
      "has spend time 121m 30s/n\n",
      "val Loss: 0.5754 Acc: 0.6863\n",
      "has spend time 121m 30s/n\n",
      "\n",
      "Epoch 3421/9999\n",
      "----------\n",
      "train Loss: 0.5461 Acc: 0.6885\n",
      "has spend time 121m 32s/n\n",
      "val Loss: 0.5586 Acc: 0.7059\n",
      "has spend time 121m 33s/n\n",
      "\n",
      "Epoch 3422/9999\n",
      "----------\n",
      "train Loss: 0.5040 Acc: 0.7254\n",
      "has spend time 121m 34s/n\n",
      "val Loss: 0.5731 Acc: 0.6928\n",
      "has spend time 121m 35s/n\n",
      "\n",
      "Epoch 3423/9999\n",
      "----------\n",
      "train Loss: 0.4893 Acc: 0.7418\n",
      "has spend time 121m 36s/n\n",
      "val Loss: 0.5568 Acc: 0.6928\n",
      "has spend time 121m 37s/n\n",
      "\n",
      "Epoch 3424/9999\n",
      "----------\n",
      "train Loss: 0.5425 Acc: 0.7254\n",
      "has spend time 121m 38s/n\n",
      "val Loss: 0.5484 Acc: 0.7190\n",
      "has spend time 121m 39s/n\n",
      "\n",
      "Epoch 3425/9999\n",
      "----------\n",
      "train Loss: 0.4800 Acc: 0.7746\n",
      "has spend time 121m 40s/n\n",
      "val Loss: 0.5495 Acc: 0.7190\n",
      "has spend time 121m 41s/n\n",
      "\n",
      "Epoch 3426/9999\n",
      "----------\n",
      "train Loss: 0.4858 Acc: 0.7541\n",
      "has spend time 121m 42s/n\n",
      "val Loss: 0.5519 Acc: 0.7124\n",
      "has spend time 121m 43s/n\n",
      "\n",
      "Epoch 3427/9999\n",
      "----------\n",
      "train Loss: 0.4687 Acc: 0.7623\n",
      "has spend time 121m 44s/n\n",
      "val Loss: 0.5529 Acc: 0.7059\n",
      "has spend time 121m 45s/n\n",
      "\n",
      "Epoch 3428/9999\n",
      "----------\n",
      "train Loss: 0.4849 Acc: 0.7582\n",
      "has spend time 121m 46s/n\n",
      "val Loss: 0.5524 Acc: 0.7059\n",
      "has spend time 121m 47s/n\n",
      "\n",
      "Epoch 3429/9999\n",
      "----------\n",
      "train Loss: 0.4782 Acc: 0.7623\n",
      "has spend time 121m 49s/n\n",
      "val Loss: 0.5514 Acc: 0.7124\n",
      "has spend time 121m 49s/n\n",
      "\n",
      "Epoch 3430/9999\n",
      "----------\n",
      "train Loss: 0.5071 Acc: 0.7336\n",
      "has spend time 121m 51s/n\n",
      "val Loss: 0.5469 Acc: 0.7059\n",
      "has spend time 121m 52s/n\n",
      "\n",
      "Epoch 3431/9999\n",
      "----------\n",
      "train Loss: 0.5322 Acc: 0.7213\n",
      "has spend time 121m 53s/n\n",
      "val Loss: 0.5422 Acc: 0.7124\n",
      "has spend time 121m 54s/n\n",
      "\n",
      "Epoch 3432/9999\n",
      "----------\n",
      "train Loss: 0.5086 Acc: 0.7746\n",
      "has spend time 121m 55s/n\n",
      "val Loss: 0.5482 Acc: 0.7124\n",
      "has spend time 121m 56s/n\n",
      "\n",
      "Epoch 3433/9999\n",
      "----------\n",
      "train Loss: 0.5191 Acc: 0.7172\n",
      "has spend time 121m 57s/n\n",
      "val Loss: 0.5599 Acc: 0.6993\n",
      "has spend time 121m 58s/n\n",
      "\n",
      "Epoch 3434/9999\n",
      "----------\n",
      "train Loss: 0.5240 Acc: 0.6926\n",
      "has spend time 121m 59s/n\n",
      "val Loss: 0.5463 Acc: 0.7255\n",
      "has spend time 121m 60s/n\n",
      "\n",
      "Epoch 3435/9999\n",
      "----------\n",
      "train Loss: 0.4910 Acc: 0.7500\n",
      "has spend time 122m 1s/n\n",
      "val Loss: 0.5465 Acc: 0.7124\n",
      "has spend time 122m 2s/n\n",
      "\n",
      "Epoch 3436/9999\n",
      "----------\n",
      "train Loss: 0.5002 Acc: 0.7418\n",
      "has spend time 122m 4s/n\n",
      "val Loss: 0.5465 Acc: 0.7190\n",
      "has spend time 122m 4s/n\n",
      "\n",
      "Epoch 3437/9999\n",
      "----------\n",
      "train Loss: 0.5237 Acc: 0.7172\n",
      "has spend time 122m 6s/n\n",
      "val Loss: 0.5533 Acc: 0.7059\n",
      "has spend time 122m 6s/n\n",
      "\n",
      "Epoch 3438/9999\n",
      "----------\n",
      "train Loss: 0.4954 Acc: 0.7172\n",
      "has spend time 122m 8s/n\n",
      "val Loss: 0.5524 Acc: 0.6993\n",
      "has spend time 122m 8s/n\n",
      "\n",
      "Epoch 3439/9999\n",
      "----------\n",
      "train Loss: 0.4780 Acc: 0.7910\n",
      "has spend time 122m 10s/n\n",
      "val Loss: 0.5553 Acc: 0.7124\n",
      "has spend time 122m 10s/n\n",
      "\n",
      "Epoch 3440/9999\n",
      "----------\n",
      "train Loss: 0.4947 Acc: 0.7459\n",
      "has spend time 122m 12s/n\n",
      "val Loss: 0.5427 Acc: 0.7190\n",
      "has spend time 122m 13s/n\n",
      "\n",
      "Epoch 3441/9999\n",
      "----------\n",
      "train Loss: 0.5038 Acc: 0.7090\n",
      "has spend time 122m 14s/n\n",
      "val Loss: 0.5471 Acc: 0.7059\n",
      "has spend time 122m 15s/n\n",
      "\n",
      "Epoch 3442/9999\n",
      "----------\n",
      "train Loss: 0.4917 Acc: 0.7377\n",
      "has spend time 122m 17s/n\n",
      "val Loss: 0.5519 Acc: 0.7124\n",
      "has spend time 122m 17s/n\n",
      "\n",
      "Epoch 3443/9999\n",
      "----------\n",
      "train Loss: 0.5025 Acc: 0.7746\n",
      "has spend time 122m 19s/n\n",
      "val Loss: 0.5548 Acc: 0.6993\n",
      "has spend time 122m 20s/n\n",
      "\n",
      "Epoch 3444/9999\n",
      "----------\n",
      "train Loss: 0.5147 Acc: 0.7418\n",
      "has spend time 122m 21s/n\n",
      "val Loss: 0.5514 Acc: 0.7124\n",
      "has spend time 122m 22s/n\n",
      "\n",
      "Epoch 3445/9999\n",
      "----------\n",
      "train Loss: 0.5125 Acc: 0.7172\n",
      "has spend time 122m 23s/n\n",
      "val Loss: 0.5494 Acc: 0.7124\n",
      "has spend time 122m 24s/n\n",
      "\n",
      "Epoch 3446/9999\n",
      "----------\n",
      "train Loss: 0.5208 Acc: 0.7295\n",
      "has spend time 122m 25s/n\n",
      "val Loss: 0.5533 Acc: 0.7190\n",
      "has spend time 122m 26s/n\n",
      "\n",
      "Epoch 3447/9999\n",
      "----------\n",
      "train Loss: 0.5270 Acc: 0.7213\n",
      "has spend time 122m 27s/n\n",
      "val Loss: 0.5614 Acc: 0.6928\n",
      "has spend time 122m 28s/n\n",
      "\n",
      "Epoch 3448/9999\n",
      "----------\n",
      "train Loss: 0.4992 Acc: 0.7664\n",
      "has spend time 122m 29s/n\n",
      "val Loss: 0.5565 Acc: 0.6928\n",
      "has spend time 122m 30s/n\n",
      "\n",
      "Epoch 3449/9999\n",
      "----------\n",
      "train Loss: 0.5031 Acc: 0.7500\n",
      "has spend time 122m 31s/n\n",
      "val Loss: 0.5484 Acc: 0.7124\n",
      "has spend time 122m 32s/n\n",
      "\n",
      "Epoch 3450/9999\n",
      "----------\n",
      "train Loss: 0.5056 Acc: 0.7459\n",
      "has spend time 122m 34s/n\n",
      "val Loss: 0.5754 Acc: 0.6928\n",
      "has spend time 122m 34s/n\n",
      "\n",
      "Epoch 3451/9999\n",
      "----------\n",
      "train Loss: 0.4839 Acc: 0.7500\n",
      "has spend time 122m 36s/n\n",
      "val Loss: 0.5548 Acc: 0.6993\n",
      "has spend time 122m 36s/n\n",
      "\n",
      "Epoch 3452/9999\n",
      "----------\n",
      "train Loss: 0.5045 Acc: 0.7418\n",
      "has spend time 122m 38s/n\n",
      "val Loss: 0.5436 Acc: 0.7190\n",
      "has spend time 122m 39s/n\n",
      "\n",
      "Epoch 3453/9999\n",
      "----------\n",
      "train Loss: 0.5157 Acc: 0.7705\n",
      "has spend time 122m 40s/n\n",
      "val Loss: 0.5543 Acc: 0.6993\n",
      "has spend time 122m 41s/n\n",
      "\n",
      "Epoch 3454/9999\n",
      "----------\n",
      "train Loss: 0.5136 Acc: 0.7090\n",
      "has spend time 122m 43s/n\n",
      "val Loss: 0.5610 Acc: 0.6928\n",
      "has spend time 122m 43s/n\n",
      "\n",
      "Epoch 3455/9999\n",
      "----------\n",
      "train Loss: 0.4958 Acc: 0.7746\n",
      "has spend time 122m 45s/n\n",
      "val Loss: 0.5433 Acc: 0.7059\n",
      "has spend time 122m 45s/n\n",
      "\n",
      "Epoch 3456/9999\n",
      "----------\n",
      "train Loss: 0.5370 Acc: 0.6762\n",
      "has spend time 122m 47s/n\n",
      "val Loss: 0.5465 Acc: 0.7124\n",
      "has spend time 122m 47s/n\n",
      "\n",
      "Epoch 3457/9999\n",
      "----------\n",
      "train Loss: 0.5046 Acc: 0.7254\n",
      "has spend time 122m 49s/n\n",
      "val Loss: 0.5425 Acc: 0.7190\n",
      "has spend time 122m 50s/n\n",
      "\n",
      "Epoch 3458/9999\n",
      "----------\n",
      "train Loss: 0.4762 Acc: 0.7664\n",
      "has spend time 122m 51s/n\n",
      "val Loss: 0.5551 Acc: 0.7059\n",
      "has spend time 122m 52s/n\n",
      "\n",
      "Epoch 3459/9999\n",
      "----------\n",
      "train Loss: 0.5094 Acc: 0.7541\n",
      "has spend time 122m 53s/n\n",
      "val Loss: 0.5584 Acc: 0.6993\n",
      "has spend time 122m 54s/n\n",
      "\n",
      "Epoch 3460/9999\n",
      "----------\n",
      "train Loss: 0.5033 Acc: 0.7459\n",
      "has spend time 122m 55s/n\n",
      "val Loss: 0.5443 Acc: 0.7124\n",
      "has spend time 122m 56s/n\n",
      "\n",
      "Epoch 3461/9999\n",
      "----------\n",
      "train Loss: 0.5055 Acc: 0.7377\n",
      "has spend time 122m 58s/n\n",
      "val Loss: 0.5456 Acc: 0.6993\n",
      "has spend time 122m 58s/n\n",
      "\n",
      "Epoch 3462/9999\n",
      "----------\n",
      "train Loss: 0.5159 Acc: 0.7623\n",
      "has spend time 122m 60s/n\n",
      "val Loss: 0.5659 Acc: 0.6993\n",
      "has spend time 123m 0s/n\n",
      "\n",
      "Epoch 3463/9999\n",
      "----------\n",
      "train Loss: 0.5393 Acc: 0.7213\n",
      "has spend time 123m 2s/n\n",
      "val Loss: 0.5496 Acc: 0.7059\n",
      "has spend time 123m 2s/n\n",
      "\n",
      "Epoch 3464/9999\n",
      "----------\n",
      "train Loss: 0.5225 Acc: 0.7213\n",
      "has spend time 123m 4s/n\n",
      "val Loss: 0.5476 Acc: 0.7124\n",
      "has spend time 123m 4s/n\n",
      "\n",
      "Epoch 3465/9999\n",
      "----------\n",
      "train Loss: 0.5287 Acc: 0.7213\n",
      "has spend time 123m 6s/n\n",
      "val Loss: 0.5466 Acc: 0.7190\n",
      "has spend time 123m 6s/n\n",
      "\n",
      "Epoch 3466/9999\n",
      "----------\n",
      "train Loss: 0.4942 Acc: 0.7377\n",
      "has spend time 123m 8s/n\n",
      "val Loss: 0.5598 Acc: 0.6928\n",
      "has spend time 123m 8s/n\n",
      "\n",
      "Epoch 3467/9999\n",
      "----------\n",
      "train Loss: 0.5062 Acc: 0.7500\n",
      "has spend time 123m 10s/n\n",
      "val Loss: 0.5384 Acc: 0.7190\n",
      "has spend time 123m 11s/n\n",
      "\n",
      "Epoch 3468/9999\n",
      "----------\n",
      "train Loss: 0.5236 Acc: 0.6885\n",
      "has spend time 123m 12s/n\n",
      "val Loss: 0.5436 Acc: 0.7190\n",
      "has spend time 123m 13s/n\n",
      "\n",
      "Epoch 3469/9999\n",
      "----------\n",
      "train Loss: 0.4805 Acc: 0.7500\n",
      "has spend time 123m 14s/n\n",
      "val Loss: 0.5512 Acc: 0.7190\n",
      "has spend time 123m 15s/n\n",
      "\n",
      "Epoch 3470/9999\n",
      "----------\n",
      "train Loss: 0.4997 Acc: 0.7336\n",
      "has spend time 123m 16s/n\n",
      "val Loss: 0.5804 Acc: 0.6993\n",
      "has spend time 123m 17s/n\n",
      "\n",
      "Epoch 3471/9999\n",
      "----------\n",
      "train Loss: 0.4915 Acc: 0.7459\n",
      "has spend time 123m 18s/n\n",
      "val Loss: 0.5625 Acc: 0.6863\n",
      "has spend time 123m 19s/n\n",
      "\n",
      "Epoch 3472/9999\n",
      "----------\n",
      "train Loss: 0.4932 Acc: 0.7746\n",
      "has spend time 123m 20s/n\n",
      "val Loss: 0.5502 Acc: 0.7124\n",
      "has spend time 123m 21s/n\n",
      "\n",
      "Epoch 3473/9999\n",
      "----------\n",
      "train Loss: 0.5245 Acc: 0.7582\n",
      "has spend time 123m 23s/n\n",
      "val Loss: 0.5575 Acc: 0.6928\n",
      "has spend time 123m 24s/n\n",
      "\n",
      "Epoch 3474/9999\n",
      "----------\n",
      "train Loss: 0.5428 Acc: 0.6885\n",
      "has spend time 123m 25s/n\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val Loss: 0.5630 Acc: 0.7059\n",
      "has spend time 123m 26s/n\n",
      "\n",
      "Epoch 3475/9999\n",
      "----------\n",
      "train Loss: 0.5163 Acc: 0.7336\n",
      "has spend time 123m 27s/n\n",
      "val Loss: 0.5549 Acc: 0.6993\n",
      "has spend time 123m 28s/n\n",
      "\n",
      "Epoch 3476/9999\n",
      "----------\n",
      "train Loss: 0.5192 Acc: 0.7254\n",
      "has spend time 123m 29s/n\n",
      "val Loss: 0.5508 Acc: 0.7124\n",
      "has spend time 123m 30s/n\n",
      "\n",
      "Epoch 3477/9999\n",
      "----------\n",
      "train Loss: 0.5379 Acc: 0.7049\n",
      "has spend time 123m 31s/n\n",
      "val Loss: 0.5536 Acc: 0.6993\n",
      "has spend time 123m 32s/n\n",
      "\n",
      "Epoch 3478/9999\n",
      "----------\n",
      "train Loss: 0.5149 Acc: 0.7459\n",
      "has spend time 123m 33s/n\n",
      "val Loss: 0.5439 Acc: 0.7124\n",
      "has spend time 123m 34s/n\n",
      "\n",
      "Epoch 3479/9999\n",
      "----------\n",
      "train Loss: 0.5219 Acc: 0.7541\n",
      "has spend time 123m 35s/n\n",
      "val Loss: 0.5396 Acc: 0.7190\n",
      "has spend time 123m 36s/n\n",
      "\n",
      "Epoch 3480/9999\n",
      "----------\n",
      "train Loss: 0.5421 Acc: 0.7213\n",
      "has spend time 123m 37s/n\n",
      "val Loss: 0.5428 Acc: 0.7190\n",
      "has spend time 123m 38s/n\n",
      "\n",
      "Epoch 3481/9999\n",
      "----------\n",
      "train Loss: 0.5048 Acc: 0.7500\n",
      "has spend time 123m 39s/n\n",
      "val Loss: 0.5442 Acc: 0.7124\n",
      "has spend time 123m 40s/n\n",
      "\n",
      "Epoch 3482/9999\n",
      "----------\n",
      "train Loss: 0.4977 Acc: 0.7500\n",
      "has spend time 123m 42s/n\n",
      "val Loss: 0.5496 Acc: 0.7190\n",
      "has spend time 123m 42s/n\n",
      "\n",
      "Epoch 3483/9999\n",
      "----------\n",
      "train Loss: 0.5101 Acc: 0.7664\n",
      "has spend time 123m 44s/n\n",
      "val Loss: 0.5622 Acc: 0.6928\n",
      "has spend time 123m 44s/n\n",
      "\n",
      "Epoch 3484/9999\n",
      "----------\n",
      "train Loss: 0.5129 Acc: 0.7418\n",
      "has spend time 123m 46s/n\n",
      "val Loss: 0.5705 Acc: 0.6993\n",
      "has spend time 123m 46s/n\n",
      "\n",
      "Epoch 3485/9999\n",
      "----------\n",
      "train Loss: 0.5277 Acc: 0.7295\n",
      "has spend time 123m 48s/n\n",
      "val Loss: 0.5456 Acc: 0.7124\n",
      "has spend time 123m 48s/n\n",
      "\n",
      "Epoch 3486/9999\n",
      "----------\n",
      "train Loss: 0.4846 Acc: 0.7500\n",
      "has spend time 123m 50s/n\n",
      "val Loss: 0.5446 Acc: 0.7124\n",
      "has spend time 123m 51s/n\n",
      "\n",
      "Epoch 3487/9999\n",
      "----------\n",
      "train Loss: 0.4825 Acc: 0.7459\n",
      "has spend time 123m 52s/n\n",
      "val Loss: 0.5451 Acc: 0.7059\n",
      "has spend time 123m 53s/n\n",
      "\n",
      "Epoch 3488/9999\n",
      "----------\n",
      "train Loss: 0.5196 Acc: 0.7336\n",
      "has spend time 123m 54s/n\n",
      "val Loss: 0.5477 Acc: 0.6928\n",
      "has spend time 123m 55s/n\n",
      "\n",
      "Epoch 3489/9999\n",
      "----------\n",
      "train Loss: 0.5085 Acc: 0.7377\n",
      "has spend time 123m 57s/n\n",
      "val Loss: 0.5487 Acc: 0.7124\n",
      "has spend time 123m 57s/n\n",
      "\n",
      "Epoch 3490/9999\n",
      "----------\n",
      "train Loss: 0.4903 Acc: 0.7623\n",
      "has spend time 123m 59s/n\n",
      "val Loss: 0.5457 Acc: 0.7059\n",
      "has spend time 123m 60s/n\n",
      "\n",
      "Epoch 3491/9999\n",
      "----------\n",
      "train Loss: 0.5041 Acc: 0.7377\n",
      "has spend time 124m 1s/n\n",
      "val Loss: 0.5588 Acc: 0.6863\n",
      "has spend time 124m 2s/n\n",
      "\n",
      "Epoch 3492/9999\n",
      "----------\n",
      "train Loss: 0.5145 Acc: 0.7418\n",
      "has spend time 124m 3s/n\n",
      "val Loss: 0.5549 Acc: 0.7059\n",
      "has spend time 124m 4s/n\n",
      "\n",
      "Epoch 3493/9999\n",
      "----------\n",
      "train Loss: 0.5250 Acc: 0.7131\n",
      "has spend time 124m 5s/n\n",
      "val Loss: 0.5755 Acc: 0.6993\n",
      "has spend time 124m 6s/n\n",
      "\n",
      "Epoch 3494/9999\n",
      "----------\n",
      "train Loss: 0.5000 Acc: 0.7582\n",
      "has spend time 124m 7s/n\n",
      "val Loss: 0.5564 Acc: 0.6928\n",
      "has spend time 124m 8s/n\n",
      "\n",
      "Epoch 3495/9999\n",
      "----------\n",
      "train Loss: 0.5126 Acc: 0.7541\n",
      "has spend time 124m 9s/n\n",
      "val Loss: 0.5612 Acc: 0.6863\n",
      "has spend time 124m 10s/n\n",
      "\n",
      "Epoch 3496/9999\n",
      "----------\n",
      "train Loss: 0.4804 Acc: 0.7664\n",
      "has spend time 124m 11s/n\n",
      "val Loss: 0.5480 Acc: 0.6993\n",
      "has spend time 124m 12s/n\n",
      "\n",
      "Epoch 3497/9999\n",
      "----------\n",
      "train Loss: 0.5035 Acc: 0.7541\n",
      "has spend time 124m 14s/n\n",
      "val Loss: 0.5489 Acc: 0.7124\n",
      "has spend time 124m 14s/n\n",
      "\n",
      "Epoch 3498/9999\n",
      "----------\n",
      "train Loss: 0.5286 Acc: 0.7295\n",
      "has spend time 124m 16s/n\n",
      "val Loss: 0.5533 Acc: 0.7190\n",
      "has spend time 124m 16s/n\n",
      "\n",
      "Epoch 3499/9999\n",
      "----------\n",
      "train Loss: 0.5158 Acc: 0.7213\n",
      "has spend time 124m 18s/n\n",
      "val Loss: 0.5642 Acc: 0.6993\n",
      "has spend time 124m 18s/n\n",
      "\n",
      "Epoch 3500/9999\n",
      "----------\n",
      "train Loss: 0.5041 Acc: 0.7336\n",
      "has spend time 124m 20s/n\n",
      "val Loss: 0.5717 Acc: 0.6863\n",
      "has spend time 124m 20s/n\n",
      "\n",
      "Epoch 3501/9999\n",
      "----------\n",
      "train Loss: 0.5120 Acc: 0.7459\n",
      "has spend time 124m 22s/n\n",
      "val Loss: 0.5560 Acc: 0.7124\n",
      "has spend time 124m 22s/n\n",
      "\n",
      "Epoch 3502/9999\n",
      "----------\n",
      "train Loss: 0.5208 Acc: 0.7254\n",
      "has spend time 124m 24s/n\n",
      "val Loss: 0.5560 Acc: 0.7059\n",
      "has spend time 124m 25s/n\n",
      "\n",
      "Epoch 3503/9999\n",
      "----------\n",
      "train Loss: 0.5180 Acc: 0.7336\n",
      "has spend time 124m 26s/n\n",
      "val Loss: 0.5480 Acc: 0.6928\n",
      "has spend time 124m 27s/n\n",
      "\n",
      "Epoch 3504/9999\n",
      "----------\n",
      "train Loss: 0.5226 Acc: 0.7664\n",
      "has spend time 124m 28s/n\n",
      "val Loss: 0.5591 Acc: 0.6928\n",
      "has spend time 124m 29s/n\n",
      "\n",
      "Epoch 3505/9999\n",
      "----------\n",
      "train Loss: 0.5164 Acc: 0.7418\n",
      "has spend time 124m 31s/n\n",
      "val Loss: 0.5572 Acc: 0.6928\n",
      "has spend time 124m 31s/n\n",
      "\n",
      "Epoch 3506/9999\n",
      "----------\n",
      "train Loss: 0.5110 Acc: 0.7377\n",
      "has spend time 124m 33s/n\n",
      "val Loss: 0.5404 Acc: 0.7059\n",
      "has spend time 124m 33s/n\n",
      "\n",
      "Epoch 3507/9999\n",
      "----------\n",
      "train Loss: 0.5217 Acc: 0.7131\n",
      "has spend time 124m 35s/n\n",
      "val Loss: 0.5405 Acc: 0.6993\n",
      "has spend time 124m 35s/n\n",
      "\n",
      "Epoch 3508/9999\n",
      "----------\n",
      "train Loss: 0.5402 Acc: 0.7295\n",
      "has spend time 124m 37s/n\n",
      "val Loss: 0.5553 Acc: 0.7059\n",
      "has spend time 124m 37s/n\n",
      "\n",
      "Epoch 3509/9999\n",
      "----------\n",
      "train Loss: 0.5034 Acc: 0.7664\n",
      "has spend time 124m 39s/n\n",
      "val Loss: 0.5492 Acc: 0.7124\n",
      "has spend time 124m 39s/n\n",
      "\n",
      "Epoch 3510/9999\n",
      "----------\n",
      "train Loss: 0.4815 Acc: 0.7664\n",
      "has spend time 124m 41s/n\n",
      "val Loss: 0.5489 Acc: 0.7059\n",
      "has spend time 124m 42s/n\n",
      "\n",
      "Epoch 3511/9999\n",
      "----------\n",
      "train Loss: 0.5228 Acc: 0.7090\n",
      "has spend time 124m 43s/n\n",
      "val Loss: 0.5443 Acc: 0.7255\n",
      "has spend time 124m 44s/n\n",
      "\n",
      "Epoch 3512/9999\n",
      "----------\n",
      "train Loss: 0.5114 Acc: 0.7787\n",
      "has spend time 124m 46s/n\n",
      "val Loss: 0.5441 Acc: 0.6993\n",
      "has spend time 124m 46s/n\n",
      "\n",
      "Epoch 3513/9999\n",
      "----------\n",
      "train Loss: 0.5304 Acc: 0.7090\n",
      "has spend time 124m 48s/n\n",
      "val Loss: 0.5683 Acc: 0.6993\n",
      "has spend time 124m 48s/n\n",
      "\n",
      "Epoch 3514/9999\n",
      "----------\n",
      "train Loss: 0.5167 Acc: 0.7418\n",
      "has spend time 124m 50s/n\n",
      "val Loss: 0.5518 Acc: 0.7059\n",
      "has spend time 124m 50s/n\n",
      "\n",
      "Epoch 3515/9999\n",
      "----------\n",
      "train Loss: 0.5117 Acc: 0.7500\n",
      "has spend time 124m 52s/n\n",
      "val Loss: 0.5516 Acc: 0.7124\n",
      "has spend time 124m 52s/n\n",
      "\n",
      "Epoch 3516/9999\n",
      "----------\n",
      "train Loss: 0.4938 Acc: 0.7500\n",
      "has spend time 124m 54s/n\n",
      "val Loss: 0.5484 Acc: 0.7124\n",
      "has spend time 124m 55s/n\n",
      "\n",
      "Epoch 3517/9999\n",
      "----------\n",
      "train Loss: 0.5232 Acc: 0.7336\n",
      "has spend time 124m 56s/n\n",
      "val Loss: 0.5694 Acc: 0.6928\n",
      "has spend time 124m 57s/n\n",
      "\n",
      "Epoch 3518/9999\n",
      "----------\n",
      "train Loss: 0.5053 Acc: 0.7336\n",
      "has spend time 124m 58s/n\n",
      "val Loss: 0.5482 Acc: 0.7124\n",
      "has spend time 124m 59s/n\n",
      "\n",
      "Epoch 3519/9999\n",
      "----------\n",
      "train Loss: 0.5170 Acc: 0.7295\n",
      "has spend time 125m 1s/n\n",
      "val Loss: 0.5535 Acc: 0.6993\n",
      "has spend time 125m 1s/n\n",
      "\n",
      "Epoch 3520/9999\n",
      "----------\n",
      "train Loss: 0.5302 Acc: 0.7213\n",
      "has spend time 125m 3s/n\n",
      "val Loss: 0.5646 Acc: 0.6928\n",
      "has spend time 125m 3s/n\n",
      "\n",
      "Epoch 3521/9999\n",
      "----------\n",
      "train Loss: 0.4763 Acc: 0.7705\n",
      "has spend time 125m 5s/n\n",
      "val Loss: 0.5462 Acc: 0.7190\n",
      "has spend time 125m 5s/n\n",
      "\n",
      "Epoch 3522/9999\n",
      "----------\n",
      "train Loss: 0.4891 Acc: 0.7418\n",
      "has spend time 125m 7s/n\n",
      "val Loss: 0.5608 Acc: 0.6993\n",
      "has spend time 125m 7s/n\n",
      "\n",
      "Epoch 3523/9999\n",
      "----------\n",
      "train Loss: 0.4938 Acc: 0.7377\n",
      "has spend time 125m 9s/n\n",
      "val Loss: 0.5557 Acc: 0.7124\n",
      "has spend time 125m 9s/n\n",
      "\n",
      "Epoch 3524/9999\n",
      "----------\n",
      "train Loss: 0.5045 Acc: 0.7254\n",
      "has spend time 125m 11s/n\n",
      "val Loss: 0.5451 Acc: 0.7190\n",
      "has spend time 125m 11s/n\n",
      "\n",
      "Epoch 3525/9999\n",
      "----------\n",
      "train Loss: 0.4911 Acc: 0.7459\n",
      "has spend time 125m 13s/n\n",
      "val Loss: 0.5486 Acc: 0.7059\n",
      "has spend time 125m 13s/n\n",
      "\n",
      "Epoch 3526/9999\n",
      "----------\n",
      "train Loss: 0.4828 Acc: 0.7582\n",
      "has spend time 125m 15s/n\n",
      "val Loss: 0.5477 Acc: 0.7059\n",
      "has spend time 125m 15s/n\n",
      "\n",
      "Epoch 3527/9999\n",
      "----------\n",
      "train Loss: 0.5169 Acc: 0.7213\n",
      "has spend time 125m 17s/n\n",
      "val Loss: 0.5462 Acc: 0.7124\n",
      "has spend time 125m 18s/n\n",
      "\n",
      "Epoch 3528/9999\n",
      "----------\n",
      "train Loss: 0.4870 Acc: 0.7623\n",
      "has spend time 125m 19s/n\n",
      "val Loss: 0.5477 Acc: 0.7255\n",
      "has spend time 125m 20s/n\n",
      "\n",
      "Epoch 3529/9999\n",
      "----------\n",
      "train Loss: 0.5229 Acc: 0.7377\n",
      "has spend time 125m 21s/n\n",
      "val Loss: 0.5564 Acc: 0.6928\n",
      "has spend time 125m 22s/n\n",
      "\n",
      "Epoch 3530/9999\n",
      "----------\n",
      "train Loss: 0.5238 Acc: 0.7582\n",
      "has spend time 125m 24s/n\n",
      "val Loss: 0.5658 Acc: 0.6928\n",
      "has spend time 125m 24s/n\n",
      "\n",
      "Epoch 3531/9999\n",
      "----------\n",
      "train Loss: 0.5120 Acc: 0.7295\n",
      "has spend time 125m 26s/n\n",
      "val Loss: 0.5533 Acc: 0.7124\n",
      "has spend time 125m 26s/n\n",
      "\n",
      "Epoch 3532/9999\n",
      "----------\n",
      "train Loss: 0.5231 Acc: 0.7213\n",
      "has spend time 125m 28s/n\n",
      "val Loss: 0.5449 Acc: 0.7124\n",
      "has spend time 125m 29s/n\n",
      "\n",
      "Epoch 3533/9999\n",
      "----------\n",
      "train Loss: 0.5009 Acc: 0.7295\n",
      "has spend time 125m 30s/n\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val Loss: 0.5545 Acc: 0.6993\n",
      "has spend time 125m 31s/n\n",
      "\n",
      "Epoch 3534/9999\n",
      "----------\n",
      "train Loss: 0.5129 Acc: 0.7336\n",
      "has spend time 125m 32s/n\n",
      "val Loss: 0.5416 Acc: 0.7190\n",
      "has spend time 125m 33s/n\n",
      "\n",
      "Epoch 3535/9999\n",
      "----------\n",
      "train Loss: 0.5090 Acc: 0.7336\n",
      "has spend time 125m 34s/n\n",
      "val Loss: 0.5573 Acc: 0.7059\n",
      "has spend time 125m 35s/n\n",
      "\n",
      "Epoch 3536/9999\n",
      "----------\n",
      "train Loss: 0.5184 Acc: 0.7377\n",
      "has spend time 125m 36s/n\n",
      "val Loss: 0.5585 Acc: 0.6993\n",
      "has spend time 125m 37s/n\n",
      "\n",
      "Epoch 3537/9999\n",
      "----------\n",
      "train Loss: 0.5280 Acc: 0.7131\n",
      "has spend time 125m 38s/n\n",
      "val Loss: 0.5517 Acc: 0.7124\n",
      "has spend time 125m 39s/n\n",
      "\n",
      "Epoch 3538/9999\n",
      "----------\n",
      "train Loss: 0.5264 Acc: 0.6803\n",
      "has spend time 125m 40s/n\n",
      "val Loss: 0.5518 Acc: 0.7190\n",
      "has spend time 125m 41s/n\n",
      "\n",
      "Epoch 3539/9999\n",
      "----------\n",
      "train Loss: 0.5013 Acc: 0.7336\n",
      "has spend time 125m 42s/n\n",
      "val Loss: 0.5436 Acc: 0.7255\n",
      "has spend time 125m 43s/n\n",
      "\n",
      "Epoch 3540/9999\n",
      "----------\n",
      "train Loss: 0.5077 Acc: 0.7295\n",
      "has spend time 125m 44s/n\n",
      "val Loss: 0.5607 Acc: 0.6993\n",
      "has spend time 125m 45s/n\n",
      "\n",
      "Epoch 3541/9999\n",
      "----------\n",
      "train Loss: 0.5186 Acc: 0.7500\n",
      "has spend time 125m 46s/n\n",
      "val Loss: 0.5500 Acc: 0.6993\n",
      "has spend time 125m 47s/n\n",
      "\n",
      "Epoch 3542/9999\n",
      "----------\n",
      "train Loss: 0.5051 Acc: 0.7459\n",
      "has spend time 125m 49s/n\n",
      "val Loss: 0.5610 Acc: 0.6928\n",
      "has spend time 125m 49s/n\n",
      "\n",
      "Epoch 3543/9999\n",
      "----------\n",
      "train Loss: 0.5086 Acc: 0.7377\n",
      "has spend time 125m 51s/n\n",
      "val Loss: 0.5507 Acc: 0.7059\n",
      "has spend time 125m 52s/n\n",
      "\n",
      "Epoch 3544/9999\n",
      "----------\n",
      "train Loss: 0.5018 Acc: 0.7377\n",
      "has spend time 125m 53s/n\n",
      "val Loss: 0.5466 Acc: 0.7124\n",
      "has spend time 125m 54s/n\n",
      "\n",
      "Epoch 3545/9999\n",
      "----------\n",
      "train Loss: 0.5122 Acc: 0.7254\n",
      "has spend time 125m 55s/n\n",
      "val Loss: 0.5596 Acc: 0.7059\n",
      "has spend time 125m 56s/n\n",
      "\n",
      "Epoch 3546/9999\n",
      "----------\n",
      "train Loss: 0.5000 Acc: 0.7336\n",
      "has spend time 125m 57s/n\n",
      "val Loss: 0.5460 Acc: 0.7255\n",
      "has spend time 125m 58s/n\n",
      "\n",
      "Epoch 3547/9999\n",
      "----------\n",
      "train Loss: 0.5173 Acc: 0.7131\n",
      "has spend time 125m 59s/n\n",
      "val Loss: 0.5593 Acc: 0.6928\n",
      "has spend time 125m 60s/n\n",
      "\n",
      "Epoch 3548/9999\n",
      "----------\n",
      "train Loss: 0.5302 Acc: 0.7254\n",
      "has spend time 126m 1s/n\n",
      "val Loss: 0.5458 Acc: 0.7190\n",
      "has spend time 126m 2s/n\n",
      "\n",
      "Epoch 3549/9999\n",
      "----------\n",
      "train Loss: 0.4911 Acc: 0.7459\n",
      "has spend time 126m 4s/n\n",
      "val Loss: 0.5724 Acc: 0.6863\n",
      "has spend time 126m 5s/n\n",
      "\n",
      "Epoch 3550/9999\n",
      "----------\n",
      "train Loss: 0.4958 Acc: 0.7377\n",
      "has spend time 126m 6s/n\n",
      "val Loss: 0.5581 Acc: 0.6993\n",
      "has spend time 126m 7s/n\n",
      "\n",
      "Epoch 3551/9999\n",
      "----------\n",
      "train Loss: 0.4846 Acc: 0.7459\n",
      "has spend time 126m 8s/n\n",
      "val Loss: 0.5614 Acc: 0.7059\n",
      "has spend time 126m 9s/n\n",
      "\n",
      "Epoch 3552/9999\n",
      "----------\n",
      "train Loss: 0.5217 Acc: 0.7336\n",
      "has spend time 126m 10s/n\n",
      "val Loss: 0.5491 Acc: 0.7190\n",
      "has spend time 126m 11s/n\n",
      "\n",
      "Epoch 3553/9999\n",
      "----------\n",
      "train Loss: 0.5088 Acc: 0.7418\n",
      "has spend time 126m 12s/n\n",
      "val Loss: 0.5501 Acc: 0.7124\n",
      "has spend time 126m 13s/n\n",
      "\n",
      "Epoch 3554/9999\n",
      "----------\n",
      "train Loss: 0.5381 Acc: 0.6926\n",
      "has spend time 126m 14s/n\n",
      "val Loss: 0.5512 Acc: 0.6993\n",
      "has spend time 126m 15s/n\n",
      "\n",
      "Epoch 3555/9999\n",
      "----------\n",
      "train Loss: 0.5144 Acc: 0.7336\n",
      "has spend time 126m 17s/n\n",
      "val Loss: 0.5561 Acc: 0.6993\n",
      "has spend time 126m 17s/n\n",
      "\n",
      "Epoch 3556/9999\n",
      "----------\n",
      "train Loss: 0.4819 Acc: 0.7623\n",
      "has spend time 126m 19s/n\n",
      "val Loss: 0.5473 Acc: 0.7190\n",
      "has spend time 126m 19s/n\n",
      "\n",
      "Epoch 3557/9999\n",
      "----------\n",
      "train Loss: 0.4923 Acc: 0.7541\n",
      "has spend time 126m 21s/n\n",
      "val Loss: 0.5544 Acc: 0.7059\n",
      "has spend time 126m 21s/n\n",
      "\n",
      "Epoch 3558/9999\n",
      "----------\n",
      "train Loss: 0.4951 Acc: 0.7705\n",
      "has spend time 126m 23s/n\n",
      "val Loss: 0.5519 Acc: 0.7059\n",
      "has spend time 126m 23s/n\n",
      "\n",
      "Epoch 3559/9999\n",
      "----------\n",
      "train Loss: 0.4858 Acc: 0.7418\n",
      "has spend time 126m 25s/n\n",
      "val Loss: 0.5503 Acc: 0.6993\n",
      "has spend time 126m 26s/n\n",
      "\n",
      "Epoch 3560/9999\n",
      "----------\n",
      "train Loss: 0.4999 Acc: 0.7623\n",
      "has spend time 126m 27s/n\n",
      "val Loss: 0.5668 Acc: 0.7059\n",
      "has spend time 126m 28s/n\n",
      "\n",
      "Epoch 3561/9999\n",
      "----------\n",
      "train Loss: 0.5329 Acc: 0.7418\n",
      "has spend time 126m 29s/n\n",
      "val Loss: 0.5544 Acc: 0.6993\n",
      "has spend time 126m 30s/n\n",
      "\n",
      "Epoch 3562/9999\n",
      "----------\n",
      "train Loss: 0.5312 Acc: 0.7295\n",
      "has spend time 126m 31s/n\n",
      "val Loss: 0.5484 Acc: 0.7124\n",
      "has spend time 126m 32s/n\n",
      "\n",
      "Epoch 3563/9999\n",
      "----------\n",
      "train Loss: 0.5367 Acc: 0.7213\n",
      "has spend time 126m 33s/n\n",
      "val Loss: 0.5400 Acc: 0.7124\n",
      "has spend time 126m 34s/n\n",
      "\n",
      "Epoch 3564/9999\n",
      "----------\n",
      "train Loss: 0.5010 Acc: 0.7418\n",
      "has spend time 126m 36s/n\n",
      "val Loss: 0.5445 Acc: 0.6993\n",
      "has spend time 126m 36s/n\n",
      "\n",
      "Epoch 3565/9999\n",
      "----------\n",
      "train Loss: 0.5025 Acc: 0.7213\n",
      "has spend time 126m 38s/n\n",
      "val Loss: 0.5603 Acc: 0.6993\n",
      "has spend time 126m 38s/n\n",
      "\n",
      "Epoch 3566/9999\n",
      "----------\n",
      "train Loss: 0.5035 Acc: 0.7541\n",
      "has spend time 126m 40s/n\n",
      "val Loss: 0.5514 Acc: 0.6928\n",
      "has spend time 126m 41s/n\n",
      "\n",
      "Epoch 3567/9999\n",
      "----------\n",
      "train Loss: 0.5023 Acc: 0.7418\n",
      "has spend time 126m 42s/n\n",
      "val Loss: 0.5475 Acc: 0.7190\n",
      "has spend time 126m 43s/n\n",
      "\n",
      "Epoch 3568/9999\n",
      "----------\n",
      "train Loss: 0.5260 Acc: 0.7254\n",
      "has spend time 126m 44s/n\n",
      "val Loss: 0.5449 Acc: 0.7255\n",
      "has spend time 126m 45s/n\n",
      "\n",
      "Epoch 3569/9999\n",
      "----------\n",
      "train Loss: 0.5243 Acc: 0.7664\n",
      "has spend time 126m 46s/n\n",
      "val Loss: 0.5400 Acc: 0.7255\n",
      "has spend time 126m 47s/n\n",
      "\n",
      "Epoch 3570/9999\n",
      "----------\n",
      "train Loss: 0.5091 Acc: 0.7377\n",
      "has spend time 126m 49s/n\n",
      "val Loss: 0.5603 Acc: 0.6928\n",
      "has spend time 126m 49s/n\n",
      "\n",
      "Epoch 3571/9999\n",
      "----------\n",
      "train Loss: 0.5114 Acc: 0.7213\n",
      "has spend time 126m 51s/n\n",
      "val Loss: 0.5542 Acc: 0.6993\n",
      "has spend time 126m 51s/n\n",
      "\n",
      "Epoch 3572/9999\n",
      "----------\n",
      "train Loss: 0.5284 Acc: 0.7377\n",
      "has spend time 126m 53s/n\n",
      "val Loss: 0.5562 Acc: 0.6993\n",
      "has spend time 126m 54s/n\n",
      "\n",
      "Epoch 3573/9999\n",
      "----------\n",
      "train Loss: 0.5010 Acc: 0.7336\n",
      "has spend time 126m 55s/n\n",
      "val Loss: 0.5502 Acc: 0.7059\n",
      "has spend time 126m 56s/n\n",
      "\n",
      "Epoch 3574/9999\n",
      "----------\n",
      "train Loss: 0.5183 Acc: 0.7213\n",
      "has spend time 126m 57s/n\n",
      "val Loss: 0.5388 Acc: 0.7190\n",
      "has spend time 126m 58s/n\n",
      "\n",
      "Epoch 3575/9999\n",
      "----------\n",
      "train Loss: 0.5100 Acc: 0.7418\n",
      "has spend time 126m 59s/n\n",
      "val Loss: 0.5551 Acc: 0.7059\n",
      "has spend time 126m 60s/n\n",
      "\n",
      "Epoch 3576/9999\n",
      "----------\n",
      "train Loss: 0.5210 Acc: 0.7582\n",
      "has spend time 127m 1s/n\n",
      "val Loss: 0.5613 Acc: 0.6993\n",
      "has spend time 127m 2s/n\n",
      "\n",
      "Epoch 3577/9999\n",
      "----------\n",
      "train Loss: 0.5264 Acc: 0.7295\n",
      "has spend time 127m 3s/n\n",
      "val Loss: 0.5512 Acc: 0.7059\n",
      "has spend time 127m 4s/n\n",
      "\n",
      "Epoch 3578/9999\n",
      "----------\n",
      "train Loss: 0.4857 Acc: 0.7541\n",
      "has spend time 127m 5s/n\n",
      "val Loss: 0.5382 Acc: 0.7190\n",
      "has spend time 127m 6s/n\n",
      "\n",
      "Epoch 3579/9999\n",
      "----------\n",
      "train Loss: 0.4824 Acc: 0.7705\n",
      "has spend time 127m 7s/n\n",
      "val Loss: 0.5473 Acc: 0.7124\n",
      "has spend time 127m 8s/n\n",
      "\n",
      "Epoch 3580/9999\n",
      "----------\n",
      "train Loss: 0.5148 Acc: 0.7705\n",
      "has spend time 127m 9s/n\n",
      "val Loss: 0.5522 Acc: 0.7124\n",
      "has spend time 127m 10s/n\n",
      "\n",
      "Epoch 3581/9999\n",
      "----------\n",
      "train Loss: 0.5292 Acc: 0.7418\n",
      "has spend time 127m 11s/n\n",
      "val Loss: 0.5547 Acc: 0.6993\n",
      "has spend time 127m 12s/n\n",
      "\n",
      "Epoch 3582/9999\n",
      "----------\n",
      "train Loss: 0.5557 Acc: 0.7377\n",
      "has spend time 127m 13s/n\n",
      "val Loss: 0.5524 Acc: 0.7124\n",
      "has spend time 127m 14s/n\n",
      "\n",
      "Epoch 3583/9999\n",
      "----------\n",
      "train Loss: 0.5321 Acc: 0.7131\n",
      "has spend time 127m 16s/n\n",
      "val Loss: 0.5572 Acc: 0.6993\n",
      "has spend time 127m 17s/n\n",
      "\n",
      "Epoch 3584/9999\n",
      "----------\n",
      "train Loss: 0.4908 Acc: 0.7377\n",
      "has spend time 127m 18s/n\n",
      "val Loss: 0.5571 Acc: 0.6993\n",
      "has spend time 127m 19s/n\n",
      "\n",
      "Epoch 3585/9999\n",
      "----------\n",
      "train Loss: 0.5054 Acc: 0.7254\n",
      "has spend time 127m 20s/n\n",
      "val Loss: 0.5421 Acc: 0.7255\n",
      "has spend time 127m 21s/n\n",
      "\n",
      "Epoch 3586/9999\n",
      "----------\n",
      "train Loss: 0.4959 Acc: 0.7459\n",
      "has spend time 127m 22s/n\n",
      "val Loss: 0.5530 Acc: 0.7124\n",
      "has spend time 127m 23s/n\n",
      "\n",
      "Epoch 3587/9999\n",
      "----------\n",
      "train Loss: 0.5219 Acc: 0.7336\n",
      "has spend time 127m 24s/n\n",
      "val Loss: 0.5378 Acc: 0.7059\n",
      "has spend time 127m 25s/n\n",
      "\n",
      "Epoch 3588/9999\n",
      "----------\n",
      "train Loss: 0.5206 Acc: 0.7377\n",
      "has spend time 127m 26s/n\n",
      "val Loss: 0.5621 Acc: 0.6928\n",
      "has spend time 127m 27s/n\n",
      "\n",
      "Epoch 3589/9999\n",
      "----------\n",
      "train Loss: 0.5090 Acc: 0.7459\n",
      "has spend time 127m 28s/n\n",
      "val Loss: 0.5569 Acc: 0.6993\n",
      "has spend time 127m 29s/n\n",
      "\n",
      "Epoch 3590/9999\n",
      "----------\n",
      "train Loss: 0.4970 Acc: 0.7541\n",
      "has spend time 127m 30s/n\n",
      "val Loss: 0.5486 Acc: 0.7124\n",
      "has spend time 127m 31s/n\n",
      "\n",
      "Epoch 3591/9999\n",
      "----------\n",
      "train Loss: 0.5210 Acc: 0.7459\n",
      "has spend time 127m 32s/n\n",
      "val Loss: 0.5471 Acc: 0.7190\n",
      "has spend time 127m 33s/n\n",
      "\n",
      "Epoch 3592/9999\n",
      "----------\n",
      "train Loss: 0.5191 Acc: 0.7213\n",
      "has spend time 127m 35s/n\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val Loss: 0.5597 Acc: 0.6928\n",
      "has spend time 127m 35s/n\n",
      "\n",
      "Epoch 3593/9999\n",
      "----------\n",
      "train Loss: 0.4846 Acc: 0.7787\n",
      "has spend time 127m 37s/n\n",
      "val Loss: 0.5553 Acc: 0.7059\n",
      "has spend time 127m 38s/n\n",
      "\n",
      "Epoch 3594/9999\n",
      "----------\n",
      "train Loss: 0.5008 Acc: 0.7336\n",
      "has spend time 127m 39s/n\n",
      "val Loss: 0.5503 Acc: 0.7059\n",
      "has spend time 127m 40s/n\n",
      "\n",
      "Epoch 3595/9999\n",
      "----------\n",
      "train Loss: 0.5077 Acc: 0.7418\n",
      "has spend time 127m 41s/n\n",
      "val Loss: 0.5473 Acc: 0.7124\n",
      "has spend time 127m 42s/n\n",
      "\n",
      "Epoch 3596/9999\n",
      "----------\n",
      "train Loss: 0.5099 Acc: 0.7295\n",
      "has spend time 127m 43s/n\n",
      "val Loss: 0.5576 Acc: 0.7124\n",
      "has spend time 127m 44s/n\n",
      "\n",
      "Epoch 3597/9999\n",
      "----------\n",
      "train Loss: 0.4988 Acc: 0.7459\n",
      "has spend time 127m 45s/n\n",
      "val Loss: 0.5577 Acc: 0.6993\n",
      "has spend time 127m 46s/n\n",
      "\n",
      "Epoch 3598/9999\n",
      "----------\n",
      "train Loss: 0.5270 Acc: 0.7131\n",
      "has spend time 127m 47s/n\n",
      "val Loss: 0.5461 Acc: 0.7124\n",
      "has spend time 127m 48s/n\n",
      "\n",
      "Epoch 3599/9999\n",
      "----------\n",
      "train Loss: 0.4963 Acc: 0.7787\n",
      "has spend time 127m 49s/n\n",
      "val Loss: 0.5529 Acc: 0.7059\n",
      "has spend time 127m 50s/n\n",
      "\n",
      "Epoch 3600/9999\n",
      "----------\n",
      "train Loss: 0.5435 Acc: 0.7131\n",
      "has spend time 127m 51s/n\n",
      "val Loss: 0.5532 Acc: 0.7124\n",
      "has spend time 127m 52s/n\n",
      "\n",
      "Epoch 3601/9999\n",
      "----------\n",
      "train Loss: 0.5273 Acc: 0.7008\n",
      "has spend time 127m 54s/n\n",
      "val Loss: 0.5510 Acc: 0.6993\n",
      "has spend time 127m 54s/n\n",
      "\n",
      "Epoch 3602/9999\n",
      "----------\n",
      "train Loss: 0.5057 Acc: 0.7131\n",
      "has spend time 127m 56s/n\n",
      "val Loss: 0.5625 Acc: 0.6993\n",
      "has spend time 127m 56s/n\n",
      "\n",
      "Epoch 3603/9999\n",
      "----------\n",
      "train Loss: 0.5211 Acc: 0.7377\n",
      "has spend time 127m 58s/n\n",
      "val Loss: 0.5435 Acc: 0.7255\n",
      "has spend time 127m 58s/n\n",
      "\n",
      "Epoch 3604/9999\n",
      "----------\n",
      "train Loss: 0.5119 Acc: 0.7336\n",
      "has spend time 127m 60s/n\n",
      "val Loss: 0.5510 Acc: 0.7059\n",
      "has spend time 128m 0s/n\n",
      "\n",
      "Epoch 3605/9999\n",
      "----------\n",
      "train Loss: 0.5195 Acc: 0.7172\n",
      "has spend time 128m 2s/n\n",
      "val Loss: 0.5644 Acc: 0.6797\n",
      "has spend time 128m 2s/n\n",
      "\n",
      "Epoch 3606/9999\n",
      "----------\n",
      "train Loss: 0.4754 Acc: 0.7582\n",
      "has spend time 128m 4s/n\n",
      "val Loss: 0.5527 Acc: 0.7059\n",
      "has spend time 128m 5s/n\n",
      "\n",
      "Epoch 3607/9999\n",
      "----------\n",
      "train Loss: 0.5121 Acc: 0.7418\n",
      "has spend time 128m 6s/n\n",
      "val Loss: 0.5424 Acc: 0.7190\n",
      "has spend time 128m 7s/n\n",
      "\n",
      "Epoch 3608/9999\n",
      "----------\n",
      "train Loss: 0.5204 Acc: 0.7418\n",
      "has spend time 128m 8s/n\n",
      "val Loss: 0.5515 Acc: 0.7059\n",
      "has spend time 128m 9s/n\n",
      "\n",
      "Epoch 3609/9999\n",
      "----------\n",
      "train Loss: 0.5277 Acc: 0.7377\n",
      "has spend time 128m 10s/n\n",
      "val Loss: 0.5493 Acc: 0.7190\n",
      "has spend time 128m 11s/n\n",
      "\n",
      "Epoch 3610/9999\n",
      "----------\n",
      "train Loss: 0.5157 Acc: 0.7500\n",
      "has spend time 128m 13s/n\n",
      "val Loss: 0.5540 Acc: 0.7059\n",
      "has spend time 128m 13s/n\n",
      "\n",
      "Epoch 3611/9999\n",
      "----------\n",
      "train Loss: 0.4868 Acc: 0.7295\n",
      "has spend time 128m 15s/n\n",
      "val Loss: 0.5456 Acc: 0.7124\n",
      "has spend time 128m 15s/n\n",
      "\n",
      "Epoch 3612/9999\n",
      "----------\n",
      "train Loss: 0.5090 Acc: 0.7377\n",
      "has spend time 128m 17s/n\n",
      "val Loss: 0.5438 Acc: 0.7255\n",
      "has spend time 128m 17s/n\n",
      "\n",
      "Epoch 3613/9999\n",
      "----------\n",
      "train Loss: 0.5084 Acc: 0.7582\n",
      "has spend time 128m 19s/n\n",
      "val Loss: 0.5386 Acc: 0.7190\n",
      "has spend time 128m 19s/n\n",
      "\n",
      "Epoch 3614/9999\n",
      "----------\n",
      "train Loss: 0.5147 Acc: 0.7295\n",
      "has spend time 128m 21s/n\n",
      "val Loss: 0.5477 Acc: 0.7255\n",
      "has spend time 128m 21s/n\n",
      "\n",
      "Epoch 3615/9999\n",
      "----------\n",
      "train Loss: 0.5074 Acc: 0.7541\n",
      "has spend time 128m 23s/n\n",
      "val Loss: 0.5507 Acc: 0.7124\n",
      "has spend time 128m 24s/n\n",
      "\n",
      "Epoch 3616/9999\n",
      "----------\n",
      "train Loss: 0.5208 Acc: 0.7213\n",
      "has spend time 128m 25s/n\n",
      "val Loss: 0.5489 Acc: 0.7124\n",
      "has spend time 128m 26s/n\n",
      "\n",
      "Epoch 3617/9999\n",
      "----------\n",
      "train Loss: 0.5279 Acc: 0.7049\n",
      "has spend time 128m 27s/n\n",
      "val Loss: 0.5626 Acc: 0.6993\n",
      "has spend time 128m 28s/n\n",
      "\n",
      "Epoch 3618/9999\n",
      "----------\n",
      "train Loss: 0.5158 Acc: 0.7336\n",
      "has spend time 128m 29s/n\n",
      "val Loss: 0.5632 Acc: 0.6993\n",
      "has spend time 128m 30s/n\n",
      "\n",
      "Epoch 3619/9999\n",
      "----------\n",
      "train Loss: 0.4923 Acc: 0.7377\n",
      "has spend time 128m 31s/n\n",
      "val Loss: 0.5580 Acc: 0.6928\n",
      "has spend time 128m 32s/n\n",
      "\n",
      "Epoch 3620/9999\n",
      "----------\n",
      "train Loss: 0.4961 Acc: 0.7459\n",
      "has spend time 128m 33s/n\n",
      "val Loss: 0.5534 Acc: 0.6928\n",
      "has spend time 128m 34s/n\n",
      "\n",
      "Epoch 3621/9999\n",
      "----------\n",
      "train Loss: 0.4773 Acc: 0.7787\n",
      "has spend time 128m 36s/n\n",
      "val Loss: 0.5517 Acc: 0.6993\n",
      "has spend time 128m 37s/n\n",
      "\n",
      "Epoch 3622/9999\n",
      "----------\n",
      "train Loss: 0.4964 Acc: 0.7172\n",
      "has spend time 128m 38s/n\n",
      "val Loss: 0.5496 Acc: 0.7059\n",
      "has spend time 128m 39s/n\n",
      "\n",
      "Epoch 3623/9999\n",
      "----------\n",
      "train Loss: 0.5035 Acc: 0.7500\n",
      "has spend time 128m 40s/n\n",
      "val Loss: 0.5474 Acc: 0.7124\n",
      "has spend time 128m 41s/n\n",
      "\n",
      "Epoch 3624/9999\n",
      "----------\n",
      "train Loss: 0.5138 Acc: 0.7541\n",
      "has spend time 128m 42s/n\n",
      "val Loss: 0.5472 Acc: 0.7190\n",
      "has spend time 128m 43s/n\n",
      "\n",
      "Epoch 3625/9999\n",
      "----------\n",
      "train Loss: 0.5146 Acc: 0.7295\n",
      "has spend time 128m 44s/n\n",
      "val Loss: 0.5604 Acc: 0.6928\n",
      "has spend time 128m 45s/n\n",
      "\n",
      "Epoch 3626/9999\n",
      "----------\n",
      "train Loss: 0.5230 Acc: 0.7418\n",
      "has spend time 128m 46s/n\n",
      "val Loss: 0.5547 Acc: 0.6928\n",
      "has spend time 128m 47s/n\n",
      "\n",
      "Epoch 3627/9999\n",
      "----------\n",
      "train Loss: 0.5051 Acc: 0.7377\n",
      "has spend time 128m 49s/n\n",
      "val Loss: 0.5465 Acc: 0.7059\n",
      "has spend time 128m 50s/n\n",
      "\n",
      "Epoch 3628/9999\n",
      "----------\n",
      "train Loss: 0.5127 Acc: 0.7705\n",
      "has spend time 128m 51s/n\n",
      "val Loss: 0.5637 Acc: 0.6993\n",
      "has spend time 128m 52s/n\n",
      "\n",
      "Epoch 3629/9999\n",
      "----------\n",
      "train Loss: 0.5243 Acc: 0.7049\n",
      "has spend time 128m 53s/n\n",
      "val Loss: 0.5495 Acc: 0.7124\n",
      "has spend time 128m 54s/n\n",
      "\n",
      "Epoch 3630/9999\n",
      "----------\n",
      "train Loss: 0.5254 Acc: 0.7582\n",
      "has spend time 128m 55s/n\n",
      "val Loss: 0.5607 Acc: 0.6993\n",
      "has spend time 128m 56s/n\n",
      "\n",
      "Epoch 3631/9999\n",
      "----------\n",
      "train Loss: 0.5159 Acc: 0.7664\n",
      "has spend time 128m 57s/n\n",
      "val Loss: 0.5519 Acc: 0.7190\n",
      "has spend time 128m 58s/n\n",
      "\n",
      "Epoch 3632/9999\n",
      "----------\n",
      "train Loss: 0.5033 Acc: 0.7295\n",
      "has spend time 128m 59s/n\n",
      "val Loss: 0.5481 Acc: 0.7124\n",
      "has spend time 128m 60s/n\n",
      "\n",
      "Epoch 3633/9999\n",
      "----------\n",
      "train Loss: 0.5216 Acc: 0.7336\n",
      "has spend time 129m 1s/n\n",
      "val Loss: 0.5546 Acc: 0.6993\n",
      "has spend time 129m 2s/n\n",
      "\n",
      "Epoch 3634/9999\n",
      "----------\n",
      "train Loss: 0.5253 Acc: 0.7377\n",
      "has spend time 129m 3s/n\n",
      "val Loss: 0.5549 Acc: 0.7059\n",
      "has spend time 129m 4s/n\n",
      "\n",
      "Epoch 3635/9999\n",
      "----------\n",
      "train Loss: 0.5113 Acc: 0.7377\n",
      "has spend time 129m 5s/n\n",
      "val Loss: 0.5702 Acc: 0.6928\n",
      "has spend time 129m 6s/n\n",
      "\n",
      "Epoch 3636/9999\n",
      "----------\n",
      "train Loss: 0.5158 Acc: 0.7418\n",
      "has spend time 129m 8s/n\n",
      "val Loss: 0.5669 Acc: 0.6797\n",
      "has spend time 129m 8s/n\n",
      "\n",
      "Epoch 3637/9999\n",
      "----------\n",
      "train Loss: 0.5008 Acc: 0.7500\n",
      "has spend time 129m 10s/n\n",
      "val Loss: 0.5565 Acc: 0.6993\n",
      "has spend time 129m 11s/n\n",
      "\n",
      "Epoch 3638/9999\n",
      "----------\n",
      "train Loss: 0.4960 Acc: 0.7459\n",
      "has spend time 129m 12s/n\n",
      "val Loss: 0.5647 Acc: 0.7059\n",
      "has spend time 129m 13s/n\n",
      "\n",
      "Epoch 3639/9999\n",
      "----------\n",
      "train Loss: 0.5242 Acc: 0.7213\n",
      "has spend time 129m 14s/n\n",
      "val Loss: 0.5513 Acc: 0.7059\n",
      "has spend time 129m 15s/n\n",
      "\n",
      "Epoch 3640/9999\n",
      "----------\n",
      "train Loss: 0.5254 Acc: 0.7131\n",
      "has spend time 129m 16s/n\n",
      "val Loss: 0.5540 Acc: 0.7255\n",
      "has spend time 129m 17s/n\n",
      "\n",
      "Epoch 3641/9999\n",
      "----------\n",
      "train Loss: 0.5101 Acc: 0.7459\n",
      "has spend time 129m 18s/n\n",
      "val Loss: 0.5568 Acc: 0.6863\n",
      "has spend time 129m 19s/n\n",
      "\n",
      "Epoch 3642/9999\n",
      "----------\n",
      "train Loss: 0.5218 Acc: 0.7131\n",
      "has spend time 129m 21s/n\n",
      "val Loss: 0.5467 Acc: 0.7059\n",
      "has spend time 129m 21s/n\n",
      "\n",
      "Epoch 3643/9999\n",
      "----------\n",
      "train Loss: 0.4958 Acc: 0.7418\n",
      "has spend time 129m 23s/n\n",
      "val Loss: 0.5416 Acc: 0.7190\n",
      "has spend time 129m 24s/n\n",
      "\n",
      "Epoch 3644/9999\n",
      "----------\n",
      "train Loss: 0.5163 Acc: 0.7418\n",
      "has spend time 129m 25s/n\n",
      "val Loss: 0.5430 Acc: 0.6993\n",
      "has spend time 129m 26s/n\n",
      "\n",
      "Epoch 3645/9999\n",
      "----------\n",
      "train Loss: 0.5083 Acc: 0.7254\n",
      "has spend time 129m 27s/n\n",
      "val Loss: 0.5498 Acc: 0.6928\n",
      "has spend time 129m 28s/n\n",
      "\n",
      "Epoch 3646/9999\n",
      "----------\n",
      "train Loss: 0.5270 Acc: 0.7336\n",
      "has spend time 129m 29s/n\n",
      "val Loss: 0.5520 Acc: 0.7124\n",
      "has spend time 129m 30s/n\n",
      "\n",
      "Epoch 3647/9999\n",
      "----------\n",
      "train Loss: 0.4818 Acc: 0.7787\n",
      "has spend time 129m 31s/n\n",
      "val Loss: 0.5480 Acc: 0.7124\n",
      "has spend time 129m 32s/n\n",
      "\n",
      "Epoch 3648/9999\n",
      "----------\n",
      "train Loss: 0.5353 Acc: 0.7008\n",
      "has spend time 129m 33s/n\n",
      "val Loss: 0.5471 Acc: 0.7124\n",
      "has spend time 129m 34s/n\n",
      "\n",
      "Epoch 3649/9999\n",
      "----------\n",
      "train Loss: 0.5433 Acc: 0.7090\n",
      "has spend time 129m 36s/n\n",
      "val Loss: 0.5499 Acc: 0.7190\n",
      "has spend time 129m 36s/n\n",
      "\n",
      "Epoch 3650/9999\n",
      "----------\n",
      "train Loss: 0.4965 Acc: 0.7500\n",
      "has spend time 129m 38s/n\n",
      "val Loss: 0.5512 Acc: 0.7059\n",
      "has spend time 129m 38s/n\n",
      "\n",
      "Epoch 3651/9999\n",
      "----------\n",
      "train Loss: 0.5325 Acc: 0.7049\n",
      "has spend time 129m 39s/n\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val Loss: 0.5472 Acc: 0.7059\n",
      "has spend time 129m 40s/n\n",
      "\n",
      "Epoch 3652/9999\n",
      "----------\n",
      "train Loss: 0.5067 Acc: 0.7295\n",
      "has spend time 129m 42s/n\n",
      "val Loss: 0.5566 Acc: 0.6928\n",
      "has spend time 129m 42s/n\n",
      "\n",
      "Epoch 3653/9999\n",
      "----------\n",
      "train Loss: 0.5009 Acc: 0.7582\n",
      "has spend time 129m 44s/n\n",
      "val Loss: 0.5582 Acc: 0.7190\n",
      "has spend time 129m 44s/n\n",
      "\n",
      "Epoch 3654/9999\n",
      "----------\n",
      "train Loss: 0.5003 Acc: 0.7377\n",
      "has spend time 129m 46s/n\n",
      "val Loss: 0.5538 Acc: 0.7059\n",
      "has spend time 129m 46s/n\n",
      "\n",
      "Epoch 3655/9999\n",
      "----------\n",
      "train Loss: 0.5130 Acc: 0.7172\n",
      "has spend time 129m 48s/n\n",
      "val Loss: 0.5583 Acc: 0.7059\n",
      "has spend time 129m 49s/n\n",
      "\n",
      "Epoch 3656/9999\n",
      "----------\n",
      "train Loss: 0.5003 Acc: 0.7295\n",
      "has spend time 129m 50s/n\n",
      "val Loss: 0.5545 Acc: 0.7124\n",
      "has spend time 129m 51s/n\n",
      "\n",
      "Epoch 3657/9999\n",
      "----------\n",
      "train Loss: 0.5200 Acc: 0.7254\n",
      "has spend time 129m 52s/n\n",
      "val Loss: 0.5622 Acc: 0.6928\n",
      "has spend time 129m 53s/n\n",
      "\n",
      "Epoch 3658/9999\n",
      "----------\n",
      "train Loss: 0.4889 Acc: 0.7049\n",
      "has spend time 129m 54s/n\n",
      "val Loss: 0.5670 Acc: 0.6928\n",
      "has spend time 129m 55s/n\n",
      "\n",
      "Epoch 3659/9999\n",
      "----------\n",
      "train Loss: 0.4817 Acc: 0.7705\n",
      "has spend time 129m 56s/n\n",
      "val Loss: 0.5608 Acc: 0.6928\n",
      "has spend time 129m 57s/n\n",
      "\n",
      "Epoch 3660/9999\n",
      "----------\n",
      "train Loss: 0.4999 Acc: 0.7500\n",
      "has spend time 129m 59s/n\n",
      "val Loss: 0.5564 Acc: 0.6993\n",
      "has spend time 129m 59s/n\n",
      "\n",
      "Epoch 3661/9999\n",
      "----------\n",
      "train Loss: 0.5406 Acc: 0.7090\n",
      "has spend time 130m 1s/n\n",
      "val Loss: 0.5487 Acc: 0.7124\n",
      "has spend time 130m 2s/n\n",
      "\n",
      "Epoch 3662/9999\n",
      "----------\n",
      "train Loss: 0.5218 Acc: 0.7336\n",
      "has spend time 130m 3s/n\n",
      "val Loss: 0.5577 Acc: 0.6863\n",
      "has spend time 130m 4s/n\n",
      "\n",
      "Epoch 3663/9999\n",
      "----------\n",
      "train Loss: 0.4662 Acc: 0.7705\n",
      "has spend time 130m 5s/n\n",
      "val Loss: 0.5506 Acc: 0.6993\n",
      "has spend time 130m 6s/n\n",
      "\n",
      "Epoch 3664/9999\n",
      "----------\n",
      "train Loss: 0.5320 Acc: 0.7172\n",
      "has spend time 130m 7s/n\n",
      "val Loss: 0.5370 Acc: 0.7190\n",
      "has spend time 130m 8s/n\n",
      "\n",
      "Epoch 3665/9999\n",
      "----------\n",
      "train Loss: 0.4919 Acc: 0.7623\n",
      "has spend time 130m 9s/n\n",
      "val Loss: 0.5497 Acc: 0.6993\n",
      "has spend time 130m 10s/n\n",
      "\n",
      "Epoch 3666/9999\n",
      "----------\n",
      "train Loss: 0.4840 Acc: 0.7623\n",
      "has spend time 130m 11s/n\n",
      "val Loss: 0.5495 Acc: 0.7059\n",
      "has spend time 130m 12s/n\n",
      "\n",
      "Epoch 3667/9999\n",
      "----------\n",
      "train Loss: 0.4998 Acc: 0.7582\n",
      "has spend time 130m 14s/n\n",
      "val Loss: 0.5538 Acc: 0.7059\n",
      "has spend time 130m 14s/n\n",
      "\n",
      "Epoch 3668/9999\n",
      "----------\n",
      "train Loss: 0.5175 Acc: 0.7377\n",
      "has spend time 130m 16s/n\n",
      "val Loss: 0.5541 Acc: 0.6928\n",
      "has spend time 130m 16s/n\n",
      "\n",
      "Epoch 3669/9999\n",
      "----------\n",
      "train Loss: 0.5075 Acc: 0.7500\n",
      "has spend time 130m 18s/n\n",
      "val Loss: 0.5461 Acc: 0.7124\n",
      "has spend time 130m 18s/n\n",
      "\n",
      "Epoch 3670/9999\n",
      "----------\n",
      "train Loss: 0.5098 Acc: 0.7295\n",
      "has spend time 130m 20s/n\n",
      "val Loss: 0.5514 Acc: 0.6993\n",
      "has spend time 130m 20s/n\n",
      "\n",
      "Epoch 3671/9999\n",
      "----------\n",
      "train Loss: 0.4927 Acc: 0.7623\n",
      "has spend time 130m 22s/n\n",
      "val Loss: 0.5449 Acc: 0.7124\n",
      "has spend time 130m 22s/n\n",
      "\n",
      "Epoch 3672/9999\n",
      "----------\n",
      "train Loss: 0.4985 Acc: 0.7377\n",
      "has spend time 130m 24s/n\n",
      "val Loss: 0.5551 Acc: 0.6993\n",
      "has spend time 130m 25s/n\n",
      "\n",
      "Epoch 3673/9999\n",
      "----------\n",
      "train Loss: 0.4811 Acc: 0.7418\n",
      "has spend time 130m 26s/n\n",
      "val Loss: 0.5486 Acc: 0.7059\n",
      "has spend time 130m 27s/n\n",
      "\n",
      "Epoch 3674/9999\n",
      "----------\n",
      "train Loss: 0.4995 Acc: 0.7541\n",
      "has spend time 130m 28s/n\n",
      "val Loss: 0.5489 Acc: 0.7124\n",
      "has spend time 130m 29s/n\n",
      "\n",
      "Epoch 3675/9999\n",
      "----------\n",
      "train Loss: 0.4981 Acc: 0.7582\n",
      "has spend time 130m 30s/n\n",
      "val Loss: 0.5497 Acc: 0.7124\n",
      "has spend time 130m 31s/n\n",
      "\n",
      "Epoch 3676/9999\n",
      "----------\n",
      "train Loss: 0.5121 Acc: 0.7418\n",
      "has spend time 130m 32s/n\n",
      "val Loss: 0.5423 Acc: 0.7190\n",
      "has spend time 130m 33s/n\n",
      "\n",
      "Epoch 3677/9999\n",
      "----------\n",
      "train Loss: 0.5001 Acc: 0.7377\n",
      "has spend time 130m 34s/n\n",
      "val Loss: 0.5460 Acc: 0.7059\n",
      "has spend time 130m 35s/n\n",
      "\n",
      "Epoch 3678/9999\n",
      "----------\n",
      "train Loss: 0.5061 Acc: 0.7008\n",
      "has spend time 130m 37s/n\n",
      "val Loss: 0.5406 Acc: 0.7190\n",
      "has spend time 130m 37s/n\n",
      "\n",
      "Epoch 3679/9999\n",
      "----------\n",
      "train Loss: 0.5028 Acc: 0.7377\n",
      "has spend time 130m 39s/n\n",
      "val Loss: 0.5630 Acc: 0.7059\n",
      "has spend time 130m 40s/n\n",
      "\n",
      "Epoch 3680/9999\n",
      "----------\n",
      "train Loss: 0.5047 Acc: 0.7541\n",
      "has spend time 130m 41s/n\n",
      "val Loss: 0.5540 Acc: 0.6993\n",
      "has spend time 130m 42s/n\n",
      "\n",
      "Epoch 3681/9999\n",
      "----------\n",
      "train Loss: 0.5190 Acc: 0.7623\n",
      "has spend time 130m 43s/n\n",
      "val Loss: 0.5538 Acc: 0.7059\n",
      "has spend time 130m 44s/n\n",
      "\n",
      "Epoch 3682/9999\n",
      "----------\n",
      "train Loss: 0.5074 Acc: 0.7090\n",
      "has spend time 130m 45s/n\n",
      "val Loss: 0.5626 Acc: 0.6993\n",
      "has spend time 130m 46s/n\n",
      "\n",
      "Epoch 3683/9999\n",
      "----------\n",
      "train Loss: 0.5209 Acc: 0.7295\n",
      "has spend time 130m 48s/n\n",
      "val Loss: 0.5521 Acc: 0.7190\n",
      "has spend time 130m 48s/n\n",
      "\n",
      "Epoch 3684/9999\n",
      "----------\n",
      "train Loss: 0.5136 Acc: 0.7582\n",
      "has spend time 130m 50s/n\n",
      "val Loss: 0.5548 Acc: 0.6928\n",
      "has spend time 130m 51s/n\n",
      "\n",
      "Epoch 3685/9999\n",
      "----------\n",
      "train Loss: 0.5189 Acc: 0.7254\n",
      "has spend time 130m 52s/n\n",
      "val Loss: 0.5465 Acc: 0.7190\n",
      "has spend time 130m 53s/n\n",
      "\n",
      "Epoch 3686/9999\n",
      "----------\n",
      "train Loss: 0.5294 Acc: 0.7295\n",
      "has spend time 130m 55s/n\n",
      "val Loss: 0.5503 Acc: 0.7059\n",
      "has spend time 130m 55s/n\n",
      "\n",
      "Epoch 3687/9999\n",
      "----------\n",
      "train Loss: 0.5196 Acc: 0.7172\n",
      "has spend time 130m 57s/n\n",
      "val Loss: 0.5471 Acc: 0.7124\n",
      "has spend time 130m 57s/n\n",
      "\n",
      "Epoch 3688/9999\n",
      "----------\n",
      "train Loss: 0.4887 Acc: 0.7500\n",
      "has spend time 130m 59s/n\n",
      "val Loss: 0.5558 Acc: 0.6993\n",
      "has spend time 130m 59s/n\n",
      "\n",
      "Epoch 3689/9999\n",
      "----------\n",
      "train Loss: 0.5300 Acc: 0.7254\n",
      "has spend time 131m 1s/n\n",
      "val Loss: 0.5660 Acc: 0.6993\n",
      "has spend time 131m 1s/n\n",
      "\n",
      "Epoch 3690/9999\n",
      "----------\n",
      "train Loss: 0.5158 Acc: 0.7418\n",
      "has spend time 131m 3s/n\n",
      "val Loss: 0.5432 Acc: 0.7190\n",
      "has spend time 131m 3s/n\n",
      "\n",
      "Epoch 3691/9999\n",
      "----------\n",
      "train Loss: 0.5270 Acc: 0.7541\n",
      "has spend time 131m 5s/n\n",
      "val Loss: 0.5396 Acc: 0.7190\n",
      "has spend time 131m 6s/n\n",
      "\n",
      "Epoch 3692/9999\n",
      "----------\n",
      "train Loss: 0.5089 Acc: 0.7377\n",
      "has spend time 131m 7s/n\n",
      "val Loss: 0.5411 Acc: 0.7190\n",
      "has spend time 131m 8s/n\n",
      "\n",
      "Epoch 3693/9999\n",
      "----------\n",
      "train Loss: 0.5127 Acc: 0.7213\n",
      "has spend time 131m 9s/n\n",
      "val Loss: 0.5413 Acc: 0.7255\n",
      "has spend time 131m 10s/n\n",
      "\n",
      "Epoch 3694/9999\n",
      "----------\n",
      "train Loss: 0.5044 Acc: 0.7254\n",
      "has spend time 131m 12s/n\n",
      "val Loss: 0.5547 Acc: 0.7059\n",
      "has spend time 131m 12s/n\n",
      "\n",
      "Epoch 3695/9999\n",
      "----------\n",
      "train Loss: 0.5065 Acc: 0.7500\n",
      "has spend time 131m 14s/n\n",
      "val Loss: 0.5509 Acc: 0.7124\n",
      "has spend time 131m 14s/n\n",
      "\n",
      "Epoch 3696/9999\n",
      "----------\n",
      "train Loss: 0.4873 Acc: 0.7500\n",
      "has spend time 131m 16s/n\n",
      "val Loss: 0.5507 Acc: 0.6993\n",
      "has spend time 131m 16s/n\n",
      "\n",
      "Epoch 3697/9999\n",
      "----------\n",
      "train Loss: 0.5024 Acc: 0.7295\n",
      "has spend time 131m 18s/n\n",
      "val Loss: 0.5477 Acc: 0.6993\n",
      "has spend time 131m 18s/n\n",
      "\n",
      "Epoch 3698/9999\n",
      "----------\n",
      "train Loss: 0.5273 Acc: 0.7049\n",
      "has spend time 131m 20s/n\n",
      "val Loss: 0.5559 Acc: 0.7059\n",
      "has spend time 131m 21s/n\n",
      "\n",
      "Epoch 3699/9999\n",
      "----------\n",
      "train Loss: 0.5226 Acc: 0.7213\n",
      "has spend time 131m 22s/n\n",
      "val Loss: 0.5426 Acc: 0.7190\n",
      "has spend time 131m 23s/n\n",
      "\n",
      "Epoch 3700/9999\n",
      "----------\n",
      "train Loss: 0.4937 Acc: 0.7418\n",
      "has spend time 131m 24s/n\n",
      "val Loss: 0.5387 Acc: 0.7124\n",
      "has spend time 131m 25s/n\n",
      "\n",
      "Epoch 3701/9999\n",
      "----------\n",
      "train Loss: 0.5199 Acc: 0.7090\n",
      "has spend time 131m 26s/n\n",
      "val Loss: 0.5595 Acc: 0.7059\n",
      "has spend time 131m 27s/n\n",
      "\n",
      "Epoch 3702/9999\n",
      "----------\n",
      "train Loss: 0.5389 Acc: 0.7131\n",
      "has spend time 131m 28s/n\n",
      "val Loss: 0.5467 Acc: 0.7059\n",
      "has spend time 131m 29s/n\n",
      "\n",
      "Epoch 3703/9999\n",
      "----------\n",
      "train Loss: 0.4752 Acc: 0.7541\n",
      "has spend time 131m 31s/n\n",
      "val Loss: 0.5429 Acc: 0.7190\n",
      "has spend time 131m 31s/n\n",
      "\n",
      "Epoch 3704/9999\n",
      "----------\n",
      "train Loss: 0.5126 Acc: 0.7090\n",
      "has spend time 131m 33s/n\n",
      "val Loss: 0.5519 Acc: 0.7124\n",
      "has spend time 131m 33s/n\n",
      "\n",
      "Epoch 3705/9999\n",
      "----------\n",
      "train Loss: 0.4980 Acc: 0.7377\n",
      "has spend time 131m 35s/n\n",
      "val Loss: 0.5527 Acc: 0.7059\n",
      "has spend time 131m 35s/n\n",
      "\n",
      "Epoch 3706/9999\n",
      "----------\n",
      "train Loss: 0.4960 Acc: 0.7377\n",
      "has spend time 131m 37s/n\n",
      "val Loss: 0.5471 Acc: 0.7059\n",
      "has spend time 131m 37s/n\n",
      "\n",
      "Epoch 3707/9999\n",
      "----------\n",
      "train Loss: 0.5115 Acc: 0.7377\n",
      "has spend time 131m 39s/n\n",
      "val Loss: 0.5448 Acc: 0.7124\n",
      "has spend time 131m 39s/n\n",
      "\n",
      "Epoch 3708/9999\n",
      "----------\n",
      "train Loss: 0.5222 Acc: 0.7090\n",
      "has spend time 131m 41s/n\n",
      "val Loss: 0.5603 Acc: 0.7059\n",
      "has spend time 131m 42s/n\n",
      "\n",
      "Epoch 3709/9999\n",
      "----------\n",
      "train Loss: 0.5004 Acc: 0.7254\n",
      "has spend time 131m 43s/n\n",
      "val Loss: 0.5472 Acc: 0.7190\n",
      "has spend time 131m 44s/n\n",
      "\n",
      "Epoch 3710/9999\n",
      "----------\n",
      "train Loss: 0.4943 Acc: 0.7500\n",
      "has spend time 131m 46s/n\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val Loss: 0.5419 Acc: 0.7059\n",
      "has spend time 131m 46s/n\n",
      "\n",
      "Epoch 3711/9999\n",
      "----------\n",
      "train Loss: 0.5048 Acc: 0.7582\n",
      "has spend time 131m 48s/n\n",
      "val Loss: 0.5429 Acc: 0.7124\n",
      "has spend time 131m 48s/n\n",
      "\n",
      "Epoch 3712/9999\n",
      "----------\n",
      "train Loss: 0.4807 Acc: 0.7500\n",
      "has spend time 131m 50s/n\n",
      "val Loss: 0.5454 Acc: 0.7124\n",
      "has spend time 131m 50s/n\n",
      "\n",
      "Epoch 3713/9999\n",
      "----------\n",
      "train Loss: 0.4959 Acc: 0.7500\n",
      "has spend time 131m 52s/n\n",
      "val Loss: 0.5530 Acc: 0.7124\n",
      "has spend time 131m 52s/n\n",
      "\n",
      "Epoch 3714/9999\n",
      "----------\n",
      "train Loss: 0.5127 Acc: 0.7336\n",
      "has spend time 131m 54s/n\n",
      "val Loss: 0.5537 Acc: 0.7059\n",
      "has spend time 131m 55s/n\n",
      "\n",
      "Epoch 3715/9999\n",
      "----------\n",
      "train Loss: 0.5191 Acc: 0.7336\n",
      "has spend time 131m 56s/n\n",
      "val Loss: 0.5498 Acc: 0.7190\n",
      "has spend time 131m 57s/n\n",
      "\n",
      "Epoch 3716/9999\n",
      "----------\n",
      "train Loss: 0.5134 Acc: 0.7377\n",
      "has spend time 131m 58s/n\n",
      "val Loss: 0.5499 Acc: 0.7124\n",
      "has spend time 131m 59s/n\n",
      "\n",
      "Epoch 3717/9999\n",
      "----------\n",
      "train Loss: 0.4885 Acc: 0.7418\n",
      "has spend time 132m 0s/n\n",
      "val Loss: 0.5522 Acc: 0.6928\n",
      "has spend time 132m 1s/n\n",
      "\n",
      "Epoch 3718/9999\n",
      "----------\n",
      "train Loss: 0.5090 Acc: 0.7541\n",
      "has spend time 132m 3s/n\n",
      "val Loss: 0.5497 Acc: 0.7059\n",
      "has spend time 132m 3s/n\n",
      "\n",
      "Epoch 3719/9999\n",
      "----------\n",
      "train Loss: 0.4929 Acc: 0.7787\n",
      "has spend time 132m 5s/n\n",
      "val Loss: 0.5504 Acc: 0.7059\n",
      "has spend time 132m 6s/n\n",
      "\n",
      "Epoch 3720/9999\n",
      "----------\n",
      "train Loss: 0.5149 Acc: 0.7418\n",
      "has spend time 132m 7s/n\n",
      "val Loss: 0.5589 Acc: 0.7124\n",
      "has spend time 132m 8s/n\n",
      "\n",
      "Epoch 3721/9999\n",
      "----------\n",
      "train Loss: 0.4745 Acc: 0.7705\n",
      "has spend time 132m 10s/n\n",
      "val Loss: 0.5558 Acc: 0.7124\n",
      "has spend time 132m 10s/n\n",
      "\n",
      "Epoch 3722/9999\n",
      "----------\n",
      "train Loss: 0.4992 Acc: 0.7090\n",
      "has spend time 132m 12s/n\n",
      "val Loss: 0.5631 Acc: 0.6993\n",
      "has spend time 132m 12s/n\n",
      "\n",
      "Epoch 3723/9999\n",
      "----------\n",
      "train Loss: 0.4942 Acc: 0.7623\n",
      "has spend time 132m 14s/n\n",
      "val Loss: 0.5505 Acc: 0.6993\n",
      "has spend time 132m 14s/n\n",
      "\n",
      "Epoch 3724/9999\n",
      "----------\n",
      "train Loss: 0.5338 Acc: 0.7336\n",
      "has spend time 132m 16s/n\n",
      "val Loss: 0.5535 Acc: 0.7124\n",
      "has spend time 132m 17s/n\n",
      "\n",
      "Epoch 3725/9999\n",
      "----------\n",
      "train Loss: 0.5275 Acc: 0.7459\n",
      "has spend time 132m 18s/n\n",
      "val Loss: 0.5509 Acc: 0.7190\n",
      "has spend time 132m 19s/n\n",
      "\n",
      "Epoch 3726/9999\n",
      "----------\n",
      "train Loss: 0.5403 Acc: 0.7049\n",
      "has spend time 132m 20s/n\n",
      "val Loss: 0.5478 Acc: 0.7059\n",
      "has spend time 132m 21s/n\n",
      "\n",
      "Epoch 3727/9999\n",
      "----------\n",
      "train Loss: 0.5020 Acc: 0.7336\n",
      "has spend time 132m 22s/n\n",
      "val Loss: 0.5595 Acc: 0.6928\n",
      "has spend time 132m 23s/n\n",
      "\n",
      "Epoch 3728/9999\n",
      "----------\n",
      "train Loss: 0.5058 Acc: 0.7295\n",
      "has spend time 132m 24s/n\n",
      "val Loss: 0.5591 Acc: 0.6993\n",
      "has spend time 132m 25s/n\n",
      "\n",
      "Epoch 3729/9999\n",
      "----------\n",
      "train Loss: 0.4943 Acc: 0.7541\n",
      "has spend time 132m 26s/n\n",
      "val Loss: 0.5447 Acc: 0.7255\n",
      "has spend time 132m 27s/n\n",
      "\n",
      "Epoch 3730/9999\n",
      "----------\n",
      "train Loss: 0.4991 Acc: 0.7377\n",
      "has spend time 132m 29s/n\n",
      "val Loss: 0.5407 Acc: 0.7059\n",
      "has spend time 132m 29s/n\n",
      "\n",
      "Epoch 3731/9999\n",
      "----------\n",
      "train Loss: 0.5206 Acc: 0.7295\n",
      "has spend time 132m 31s/n\n",
      "val Loss: 0.5470 Acc: 0.7059\n",
      "has spend time 132m 32s/n\n",
      "\n",
      "Epoch 3732/9999\n",
      "----------\n",
      "train Loss: 0.5099 Acc: 0.7254\n",
      "has spend time 132m 33s/n\n",
      "val Loss: 0.5469 Acc: 0.6993\n",
      "has spend time 132m 34s/n\n",
      "\n",
      "Epoch 3733/9999\n",
      "----------\n",
      "train Loss: 0.5309 Acc: 0.7377\n",
      "has spend time 132m 35s/n\n",
      "val Loss: 0.5538 Acc: 0.6993\n",
      "has spend time 132m 36s/n\n",
      "\n",
      "Epoch 3734/9999\n",
      "----------\n",
      "train Loss: 0.4890 Acc: 0.7869\n",
      "has spend time 132m 37s/n\n",
      "val Loss: 0.5503 Acc: 0.7124\n",
      "has spend time 132m 38s/n\n",
      "\n",
      "Epoch 3735/9999\n",
      "----------\n",
      "train Loss: 0.4817 Acc: 0.7582\n",
      "has spend time 132m 39s/n\n",
      "val Loss: 0.5509 Acc: 0.7059\n",
      "has spend time 132m 40s/n\n",
      "\n",
      "Epoch 3736/9999\n",
      "----------\n",
      "train Loss: 0.4655 Acc: 0.7746\n",
      "has spend time 132m 42s/n\n",
      "val Loss: 0.5456 Acc: 0.7124\n",
      "has spend time 132m 43s/n\n",
      "\n",
      "Epoch 3737/9999\n",
      "----------\n",
      "train Loss: 0.5100 Acc: 0.7500\n",
      "has spend time 132m 44s/n\n",
      "val Loss: 0.5545 Acc: 0.6863\n",
      "has spend time 132m 45s/n\n",
      "\n",
      "Epoch 3738/9999\n",
      "----------\n",
      "train Loss: 0.5318 Acc: 0.6803\n",
      "has spend time 132m 46s/n\n",
      "val Loss: 0.5527 Acc: 0.7059\n",
      "has spend time 132m 47s/n\n",
      "\n",
      "Epoch 3739/9999\n",
      "----------\n",
      "train Loss: 0.4866 Acc: 0.7131\n",
      "has spend time 132m 48s/n\n",
      "val Loss: 0.5566 Acc: 0.7059\n",
      "has spend time 132m 49s/n\n",
      "\n",
      "Epoch 3740/9999\n",
      "----------\n",
      "train Loss: 0.5239 Acc: 0.7213\n",
      "has spend time 132m 50s/n\n",
      "val Loss: 0.5630 Acc: 0.6993\n",
      "has spend time 132m 51s/n\n",
      "\n",
      "Epoch 3741/9999\n",
      "----------\n",
      "train Loss: 0.5184 Acc: 0.7377\n",
      "has spend time 132m 52s/n\n",
      "val Loss: 0.5485 Acc: 0.7124\n",
      "has spend time 132m 53s/n\n",
      "\n",
      "Epoch 3742/9999\n",
      "----------\n",
      "train Loss: 0.5016 Acc: 0.7459\n",
      "has spend time 132m 55s/n\n",
      "val Loss: 0.5465 Acc: 0.7124\n",
      "has spend time 132m 55s/n\n",
      "\n",
      "Epoch 3743/9999\n",
      "----------\n",
      "train Loss: 0.5007 Acc: 0.7418\n",
      "has spend time 132m 57s/n\n",
      "val Loss: 0.5772 Acc: 0.6863\n",
      "has spend time 132m 57s/n\n",
      "\n",
      "Epoch 3744/9999\n",
      "----------\n",
      "train Loss: 0.5043 Acc: 0.7213\n",
      "has spend time 132m 59s/n\n",
      "val Loss: 0.5571 Acc: 0.7059\n",
      "has spend time 132m 59s/n\n",
      "\n",
      "Epoch 3745/9999\n",
      "----------\n",
      "train Loss: 0.4927 Acc: 0.7623\n",
      "has spend time 133m 1s/n\n",
      "val Loss: 0.5691 Acc: 0.6993\n",
      "has spend time 133m 1s/n\n",
      "\n",
      "Epoch 3746/9999\n",
      "----------\n",
      "train Loss: 0.4860 Acc: 0.7254\n",
      "has spend time 133m 3s/n\n",
      "val Loss: 0.5640 Acc: 0.6993\n",
      "has spend time 133m 3s/n\n",
      "\n",
      "Epoch 3747/9999\n",
      "----------\n",
      "train Loss: 0.4952 Acc: 0.7418\n",
      "has spend time 133m 5s/n\n",
      "val Loss: 0.5473 Acc: 0.7124\n",
      "has spend time 133m 5s/n\n",
      "\n",
      "Epoch 3748/9999\n",
      "----------\n",
      "train Loss: 0.4942 Acc: 0.7336\n",
      "has spend time 133m 7s/n\n",
      "val Loss: 0.5462 Acc: 0.6928\n",
      "has spend time 133m 7s/n\n",
      "\n",
      "Epoch 3749/9999\n",
      "----------\n",
      "train Loss: 0.4949 Acc: 0.7664\n",
      "has spend time 133m 9s/n\n",
      "val Loss: 0.5569 Acc: 0.7059\n",
      "has spend time 133m 9s/n\n",
      "\n",
      "Epoch 3750/9999\n",
      "----------\n",
      "train Loss: 0.4888 Acc: 0.7910\n",
      "has spend time 133m 11s/n\n",
      "val Loss: 0.5468 Acc: 0.7059\n",
      "has spend time 133m 12s/n\n",
      "\n",
      "Epoch 3751/9999\n",
      "----------\n",
      "train Loss: 0.5121 Acc: 0.7541\n",
      "has spend time 133m 13s/n\n",
      "val Loss: 0.5433 Acc: 0.7059\n",
      "has spend time 133m 14s/n\n",
      "\n",
      "Epoch 3752/9999\n",
      "----------\n",
      "train Loss: 0.5148 Acc: 0.7254\n",
      "has spend time 133m 16s/n\n",
      "val Loss: 0.5456 Acc: 0.7124\n",
      "has spend time 133m 16s/n\n",
      "\n",
      "Epoch 3753/9999\n",
      "----------\n",
      "train Loss: 0.5043 Acc: 0.7254\n",
      "has spend time 133m 18s/n\n",
      "val Loss: 0.5425 Acc: 0.7255\n",
      "has spend time 133m 18s/n\n",
      "\n",
      "Epoch 3754/9999\n",
      "----------\n",
      "train Loss: 0.5139 Acc: 0.7090\n",
      "has spend time 133m 20s/n\n",
      "val Loss: 0.5501 Acc: 0.7059\n",
      "has spend time 133m 20s/n\n",
      "\n",
      "Epoch 3755/9999\n",
      "----------\n",
      "train Loss: 0.5145 Acc: 0.7213\n",
      "has spend time 133m 22s/n\n",
      "val Loss: 0.5426 Acc: 0.7190\n",
      "has spend time 133m 22s/n\n",
      "\n",
      "Epoch 3756/9999\n",
      "----------\n",
      "train Loss: 0.5616 Acc: 0.7254\n",
      "has spend time 133m 24s/n\n",
      "val Loss: 0.5515 Acc: 0.6993\n",
      "has spend time 133m 25s/n\n",
      "\n",
      "Epoch 3757/9999\n",
      "----------\n",
      "train Loss: 0.5616 Acc: 0.7213\n",
      "has spend time 133m 26s/n\n",
      "val Loss: 0.5515 Acc: 0.7255\n",
      "has spend time 133m 27s/n\n",
      "\n",
      "Epoch 3758/9999\n",
      "----------\n",
      "train Loss: 0.5380 Acc: 0.7131\n",
      "has spend time 133m 28s/n\n",
      "val Loss: 0.5527 Acc: 0.7059\n",
      "has spend time 133m 29s/n\n",
      "\n",
      "Epoch 3759/9999\n",
      "----------\n",
      "train Loss: 0.5009 Acc: 0.7541\n",
      "has spend time 133m 31s/n\n",
      "val Loss: 0.5497 Acc: 0.6993\n",
      "has spend time 133m 31s/n\n",
      "\n",
      "Epoch 3760/9999\n",
      "----------\n",
      "train Loss: 0.4932 Acc: 0.7459\n",
      "has spend time 133m 33s/n\n",
      "val Loss: 0.5513 Acc: 0.7059\n",
      "has spend time 133m 33s/n\n",
      "\n",
      "Epoch 3761/9999\n",
      "----------\n",
      "train Loss: 0.4936 Acc: 0.7500\n",
      "has spend time 133m 35s/n\n",
      "val Loss: 0.5615 Acc: 0.6928\n",
      "has spend time 133m 35s/n\n",
      "\n",
      "Epoch 3762/9999\n",
      "----------\n",
      "train Loss: 0.4983 Acc: 0.7582\n",
      "has spend time 133m 37s/n\n",
      "val Loss: 0.5495 Acc: 0.7059\n",
      "has spend time 133m 37s/n\n",
      "\n",
      "Epoch 3763/9999\n",
      "----------\n",
      "train Loss: 0.5039 Acc: 0.7295\n",
      "has spend time 133m 39s/n\n",
      "val Loss: 0.5472 Acc: 0.7124\n",
      "has spend time 133m 39s/n\n",
      "\n",
      "Epoch 3764/9999\n",
      "----------\n",
      "train Loss: 0.4969 Acc: 0.7336\n",
      "has spend time 133m 41s/n\n",
      "val Loss: 0.5443 Acc: 0.7190\n",
      "has spend time 133m 42s/n\n",
      "\n",
      "Epoch 3765/9999\n",
      "----------\n",
      "train Loss: 0.4763 Acc: 0.7623\n",
      "has spend time 133m 43s/n\n",
      "val Loss: 0.5488 Acc: 0.7124\n",
      "has spend time 133m 44s/n\n",
      "\n",
      "Epoch 3766/9999\n",
      "----------\n",
      "train Loss: 0.5077 Acc: 0.7131\n",
      "has spend time 133m 45s/n\n",
      "val Loss: 0.5469 Acc: 0.7124\n",
      "has spend time 133m 46s/n\n",
      "\n",
      "Epoch 3767/9999\n",
      "----------\n",
      "train Loss: 0.4850 Acc: 0.7418\n",
      "has spend time 133m 47s/n\n",
      "val Loss: 0.5533 Acc: 0.7190\n",
      "has spend time 133m 48s/n\n",
      "\n",
      "Epoch 3768/9999\n",
      "----------\n",
      "train Loss: 0.5136 Acc: 0.7131\n",
      "has spend time 133m 50s/n\n",
      "val Loss: 0.5653 Acc: 0.6993\n",
      "has spend time 133m 50s/n\n",
      "\n",
      "Epoch 3769/9999\n",
      "----------\n",
      "train Loss: 0.5041 Acc: 0.7418\n",
      "has spend time 133m 52s/n\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val Loss: 0.5575 Acc: 0.7059\n",
      "has spend time 133m 53s/n\n",
      "\n",
      "Epoch 3770/9999\n",
      "----------\n",
      "train Loss: 0.5383 Acc: 0.7336\n",
      "has spend time 133m 54s/n\n",
      "val Loss: 0.5567 Acc: 0.6993\n",
      "has spend time 133m 55s/n\n",
      "\n",
      "Epoch 3771/9999\n",
      "----------\n",
      "train Loss: 0.5419 Acc: 0.7172\n",
      "has spend time 133m 56s/n\n",
      "val Loss: 0.5478 Acc: 0.7190\n",
      "has spend time 133m 57s/n\n",
      "\n",
      "Epoch 3772/9999\n",
      "----------\n",
      "train Loss: 0.4793 Acc: 0.7664\n",
      "has spend time 133m 58s/n\n",
      "val Loss: 0.5501 Acc: 0.7190\n",
      "has spend time 133m 59s/n\n",
      "\n",
      "Epoch 3773/9999\n",
      "----------\n",
      "train Loss: 0.5291 Acc: 0.7008\n",
      "has spend time 134m 0s/n\n",
      "val Loss: 0.5566 Acc: 0.7059\n",
      "has spend time 134m 1s/n\n",
      "\n",
      "Epoch 3774/9999\n",
      "----------\n",
      "train Loss: 0.5038 Acc: 0.7295\n",
      "has spend time 134m 2s/n\n",
      "val Loss: 0.5541 Acc: 0.7059\n",
      "has spend time 134m 3s/n\n",
      "\n",
      "Epoch 3775/9999\n",
      "----------\n",
      "train Loss: 0.5020 Acc: 0.7254\n",
      "has spend time 134m 5s/n\n",
      "val Loss: 0.5463 Acc: 0.7124\n",
      "has spend time 134m 5s/n\n",
      "\n",
      "Epoch 3776/9999\n",
      "----------\n",
      "train Loss: 0.5250 Acc: 0.7172\n",
      "has spend time 134m 7s/n\n",
      "val Loss: 0.5486 Acc: 0.7059\n",
      "has spend time 134m 7s/n\n",
      "\n",
      "Epoch 3777/9999\n",
      "----------\n",
      "train Loss: 0.4910 Acc: 0.7623\n",
      "has spend time 134m 9s/n\n",
      "val Loss: 0.5680 Acc: 0.6993\n",
      "has spend time 134m 9s/n\n",
      "\n",
      "Epoch 3778/9999\n",
      "----------\n",
      "train Loss: 0.4855 Acc: 0.7582\n",
      "has spend time 134m 11s/n\n",
      "val Loss: 0.5449 Acc: 0.7190\n",
      "has spend time 134m 11s/n\n",
      "\n",
      "Epoch 3779/9999\n",
      "----------\n",
      "train Loss: 0.4928 Acc: 0.7541\n",
      "has spend time 134m 13s/n\n",
      "val Loss: 0.5597 Acc: 0.6928\n",
      "has spend time 134m 13s/n\n",
      "\n",
      "Epoch 3780/9999\n",
      "----------\n",
      "train Loss: 0.4759 Acc: 0.7746\n",
      "has spend time 134m 15s/n\n",
      "val Loss: 0.5560 Acc: 0.6993\n",
      "has spend time 134m 15s/n\n",
      "\n",
      "Epoch 3781/9999\n",
      "----------\n",
      "train Loss: 0.4935 Acc: 0.7459\n",
      "has spend time 134m 17s/n\n",
      "val Loss: 0.5574 Acc: 0.7124\n",
      "has spend time 134m 17s/n\n",
      "\n",
      "Epoch 3782/9999\n",
      "----------\n",
      "train Loss: 0.5328 Acc: 0.7254\n",
      "has spend time 134m 19s/n\n",
      "val Loss: 0.5475 Acc: 0.7124\n",
      "has spend time 134m 19s/n\n",
      "\n",
      "Epoch 3783/9999\n",
      "----------\n",
      "train Loss: 0.4764 Acc: 0.7377\n",
      "has spend time 134m 21s/n\n",
      "val Loss: 0.5494 Acc: 0.7124\n",
      "has spend time 134m 21s/n\n",
      "\n",
      "Epoch 3784/9999\n",
      "----------\n",
      "train Loss: 0.5035 Acc: 0.7664\n",
      "has spend time 134m 23s/n\n",
      "val Loss: 0.5440 Acc: 0.7190\n",
      "has spend time 134m 24s/n\n",
      "\n",
      "Epoch 3785/9999\n",
      "----------\n",
      "train Loss: 0.4883 Acc: 0.7295\n",
      "has spend time 134m 25s/n\n",
      "val Loss: 0.5408 Acc: 0.7320\n",
      "has spend time 134m 26s/n\n",
      "\n",
      "Epoch 3786/9999\n",
      "----------\n",
      "train Loss: 0.4861 Acc: 0.7623\n",
      "has spend time 134m 28s/n\n",
      "val Loss: 0.5522 Acc: 0.6993\n",
      "has spend time 134m 28s/n\n",
      "\n",
      "Epoch 3787/9999\n",
      "----------\n",
      "train Loss: 0.4846 Acc: 0.7541\n",
      "has spend time 134m 30s/n\n",
      "val Loss: 0.5436 Acc: 0.7124\n",
      "has spend time 134m 30s/n\n",
      "\n",
      "Epoch 3788/9999\n",
      "----------\n",
      "train Loss: 0.4997 Acc: 0.7418\n",
      "has spend time 134m 32s/n\n",
      "val Loss: 0.5552 Acc: 0.7059\n",
      "has spend time 134m 32s/n\n",
      "\n",
      "Epoch 3789/9999\n",
      "----------\n",
      "train Loss: 0.5092 Acc: 0.7295\n",
      "has spend time 134m 34s/n\n",
      "val Loss: 0.5533 Acc: 0.7124\n",
      "has spend time 134m 34s/n\n",
      "\n",
      "Epoch 3790/9999\n",
      "----------\n",
      "train Loss: 0.5037 Acc: 0.7500\n",
      "has spend time 134m 36s/n\n",
      "val Loss: 0.5470 Acc: 0.7190\n",
      "has spend time 134m 36s/n\n",
      "\n",
      "Epoch 3791/9999\n",
      "----------\n",
      "train Loss: 0.4918 Acc: 0.7623\n",
      "has spend time 134m 38s/n\n",
      "val Loss: 0.5444 Acc: 0.7059\n",
      "has spend time 134m 38s/n\n",
      "\n",
      "Epoch 3792/9999\n",
      "----------\n",
      "train Loss: 0.5000 Acc: 0.7377\n",
      "has spend time 134m 40s/n\n",
      "val Loss: 0.5504 Acc: 0.7124\n",
      "has spend time 134m 41s/n\n",
      "\n",
      "Epoch 3793/9999\n",
      "----------\n",
      "train Loss: 0.5206 Acc: 0.7336\n",
      "has spend time 134m 42s/n\n",
      "val Loss: 0.5598 Acc: 0.7059\n",
      "has spend time 134m 43s/n\n",
      "\n",
      "Epoch 3794/9999\n",
      "----------\n",
      "train Loss: 0.5007 Acc: 0.7459\n",
      "has spend time 134m 44s/n\n",
      "val Loss: 0.5604 Acc: 0.6993\n",
      "has spend time 134m 45s/n\n",
      "\n",
      "Epoch 3795/9999\n",
      "----------\n",
      "train Loss: 0.5290 Acc: 0.6844\n",
      "has spend time 134m 46s/n\n",
      "val Loss: 0.5504 Acc: 0.6928\n",
      "has spend time 134m 47s/n\n",
      "\n",
      "Epoch 3796/9999\n",
      "----------\n",
      "train Loss: 0.5322 Acc: 0.7172\n",
      "has spend time 134m 49s/n\n",
      "val Loss: 0.5684 Acc: 0.6928\n",
      "has spend time 134m 49s/n\n",
      "\n",
      "Epoch 3797/9999\n",
      "----------\n",
      "train Loss: 0.4995 Acc: 0.7500\n",
      "has spend time 134m 51s/n\n",
      "val Loss: 0.5497 Acc: 0.7124\n",
      "has spend time 134m 51s/n\n",
      "\n",
      "Epoch 3798/9999\n",
      "----------\n",
      "train Loss: 0.5017 Acc: 0.7664\n",
      "has spend time 134m 53s/n\n",
      "val Loss: 0.5432 Acc: 0.7124\n",
      "has spend time 134m 54s/n\n",
      "\n",
      "Epoch 3799/9999\n",
      "----------\n",
      "train Loss: 0.4892 Acc: 0.7910\n",
      "has spend time 134m 55s/n\n",
      "val Loss: 0.5517 Acc: 0.6993\n",
      "has spend time 134m 56s/n\n",
      "\n",
      "Epoch 3800/9999\n",
      "----------\n",
      "train Loss: 0.5007 Acc: 0.7500\n",
      "has spend time 134m 58s/n\n",
      "val Loss: 0.5442 Acc: 0.7124\n",
      "has spend time 134m 58s/n\n",
      "\n",
      "Epoch 3801/9999\n",
      "----------\n",
      "train Loss: 0.4972 Acc: 0.7664\n",
      "has spend time 134m 60s/n\n",
      "val Loss: 0.5471 Acc: 0.6928\n",
      "has spend time 135m 0s/n\n",
      "\n",
      "Epoch 3802/9999\n",
      "----------\n",
      "train Loss: 0.5091 Acc: 0.7418\n",
      "has spend time 135m 2s/n\n",
      "val Loss: 0.5470 Acc: 0.7059\n",
      "has spend time 135m 2s/n\n",
      "\n",
      "Epoch 3803/9999\n",
      "----------\n",
      "train Loss: 0.4847 Acc: 0.7254\n",
      "has spend time 135m 4s/n\n",
      "val Loss: 0.5602 Acc: 0.6993\n",
      "has spend time 135m 4s/n\n",
      "\n",
      "Epoch 3804/9999\n",
      "----------\n",
      "train Loss: 0.4857 Acc: 0.7582\n",
      "has spend time 135m 6s/n\n",
      "val Loss: 0.5413 Acc: 0.7190\n",
      "has spend time 135m 6s/n\n",
      "\n",
      "Epoch 3805/9999\n",
      "----------\n",
      "train Loss: 0.5421 Acc: 0.7459\n",
      "has spend time 135m 8s/n\n",
      "val Loss: 0.5493 Acc: 0.6993\n",
      "has spend time 135m 8s/n\n",
      "\n",
      "Epoch 3806/9999\n",
      "----------\n",
      "train Loss: 0.5159 Acc: 0.7008\n",
      "has spend time 135m 10s/n\n",
      "val Loss: 0.5755 Acc: 0.7059\n",
      "has spend time 135m 10s/n\n",
      "\n",
      "Epoch 3807/9999\n",
      "----------\n",
      "train Loss: 0.5049 Acc: 0.7500\n",
      "has spend time 135m 12s/n\n",
      "val Loss: 0.5624 Acc: 0.7124\n",
      "has spend time 135m 12s/n\n",
      "\n",
      "Epoch 3808/9999\n",
      "----------\n",
      "train Loss: 0.4951 Acc: 0.7541\n",
      "has spend time 135m 14s/n\n",
      "val Loss: 0.5558 Acc: 0.7124\n",
      "has spend time 135m 14s/n\n",
      "\n",
      "Epoch 3809/9999\n",
      "----------\n",
      "train Loss: 0.4835 Acc: 0.7664\n",
      "has spend time 135m 16s/n\n",
      "val Loss: 0.5518 Acc: 0.6928\n",
      "has spend time 135m 17s/n\n",
      "\n",
      "Epoch 3810/9999\n",
      "----------\n",
      "train Loss: 0.5111 Acc: 0.7336\n",
      "has spend time 135m 18s/n\n",
      "val Loss: 0.5503 Acc: 0.7124\n",
      "has spend time 135m 19s/n\n",
      "\n",
      "Epoch 3811/9999\n",
      "----------\n",
      "train Loss: 0.5019 Acc: 0.7418\n",
      "has spend time 135m 20s/n\n",
      "val Loss: 0.5503 Acc: 0.7124\n",
      "has spend time 135m 21s/n\n",
      "\n",
      "Epoch 3812/9999\n",
      "----------\n",
      "train Loss: 0.4987 Acc: 0.7459\n",
      "has spend time 135m 22s/n\n",
      "val Loss: 0.5548 Acc: 0.7124\n",
      "has spend time 135m 23s/n\n",
      "\n",
      "Epoch 3813/9999\n",
      "----------\n",
      "train Loss: 0.4965 Acc: 0.7623\n",
      "has spend time 135m 24s/n\n",
      "val Loss: 0.5535 Acc: 0.7190\n",
      "has spend time 135m 25s/n\n",
      "\n",
      "Epoch 3814/9999\n",
      "----------\n",
      "train Loss: 0.5176 Acc: 0.7213\n",
      "has spend time 135m 26s/n\n",
      "val Loss: 0.5411 Acc: 0.7190\n",
      "has spend time 135m 27s/n\n",
      "\n",
      "Epoch 3815/9999\n",
      "----------\n",
      "train Loss: 0.5310 Acc: 0.7213\n",
      "has spend time 135m 29s/n\n",
      "val Loss: 0.5525 Acc: 0.7124\n",
      "has spend time 135m 30s/n\n",
      "\n",
      "Epoch 3816/9999\n",
      "----------\n",
      "train Loss: 0.5109 Acc: 0.7459\n",
      "has spend time 135m 31s/n\n",
      "val Loss: 0.5501 Acc: 0.7124\n",
      "has spend time 135m 32s/n\n",
      "\n",
      "Epoch 3817/9999\n",
      "----------\n",
      "train Loss: 0.5248 Acc: 0.7254\n",
      "has spend time 135m 33s/n\n",
      "val Loss: 0.5510 Acc: 0.7124\n",
      "has spend time 135m 34s/n\n",
      "\n",
      "Epoch 3818/9999\n",
      "----------\n",
      "train Loss: 0.4775 Acc: 0.7746\n",
      "has spend time 135m 35s/n\n",
      "val Loss: 0.5529 Acc: 0.6928\n",
      "has spend time 135m 36s/n\n",
      "\n",
      "Epoch 3819/9999\n",
      "----------\n",
      "train Loss: 0.5131 Acc: 0.7541\n",
      "has spend time 135m 37s/n\n",
      "val Loss: 0.5483 Acc: 0.7059\n",
      "has spend time 135m 38s/n\n",
      "\n",
      "Epoch 3820/9999\n",
      "----------\n",
      "train Loss: 0.5385 Acc: 0.7172\n",
      "has spend time 135m 39s/n\n",
      "val Loss: 0.5493 Acc: 0.7190\n",
      "has spend time 135m 40s/n\n",
      "\n",
      "Epoch 3821/9999\n",
      "----------\n",
      "train Loss: 0.4737 Acc: 0.7664\n",
      "has spend time 135m 42s/n\n",
      "val Loss: 0.5486 Acc: 0.7124\n",
      "has spend time 135m 42s/n\n",
      "\n",
      "Epoch 3822/9999\n",
      "----------\n",
      "train Loss: 0.4950 Acc: 0.7500\n",
      "has spend time 135m 44s/n\n",
      "val Loss: 0.5456 Acc: 0.7255\n",
      "has spend time 135m 45s/n\n",
      "\n",
      "Epoch 3823/9999\n",
      "----------\n",
      "train Loss: 0.4959 Acc: 0.7336\n",
      "has spend time 135m 46s/n\n",
      "val Loss: 0.5516 Acc: 0.7190\n",
      "has spend time 135m 47s/n\n",
      "\n",
      "Epoch 3824/9999\n",
      "----------\n",
      "train Loss: 0.4934 Acc: 0.7623\n",
      "has spend time 135m 49s/n\n",
      "val Loss: 0.5636 Acc: 0.6928\n",
      "has spend time 135m 49s/n\n",
      "\n",
      "Epoch 3825/9999\n",
      "----------\n",
      "train Loss: 0.5404 Acc: 0.6926\n",
      "has spend time 135m 51s/n\n",
      "val Loss: 0.5463 Acc: 0.7124\n",
      "has spend time 135m 51s/n\n",
      "\n",
      "Epoch 3826/9999\n",
      "----------\n",
      "train Loss: 0.4747 Acc: 0.7705\n",
      "has spend time 135m 53s/n\n",
      "val Loss: 0.5561 Acc: 0.6993\n",
      "has spend time 135m 54s/n\n",
      "\n",
      "Epoch 3827/9999\n",
      "----------\n",
      "train Loss: 0.4935 Acc: 0.7582\n",
      "has spend time 135m 55s/n\n",
      "val Loss: 0.5545 Acc: 0.6993\n",
      "has spend time 135m 56s/n\n",
      "\n",
      "Epoch 3828/9999\n",
      "----------\n",
      "train Loss: 0.5213 Acc: 0.7541\n",
      "has spend time 135m 57s/n\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val Loss: 0.5648 Acc: 0.6993\n",
      "has spend time 135m 58s/n\n",
      "\n",
      "Epoch 3829/9999\n",
      "----------\n",
      "train Loss: 0.5132 Acc: 0.7377\n",
      "has spend time 135m 59s/n\n",
      "val Loss: 0.5580 Acc: 0.7059\n",
      "has spend time 135m 60s/n\n",
      "\n",
      "Epoch 3830/9999\n",
      "----------\n",
      "train Loss: 0.5067 Acc: 0.7172\n",
      "has spend time 136m 1s/n\n",
      "val Loss: 0.5481 Acc: 0.7124\n",
      "has spend time 136m 2s/n\n",
      "\n",
      "Epoch 3831/9999\n",
      "----------\n",
      "train Loss: 0.5104 Acc: 0.7377\n",
      "has spend time 136m 3s/n\n",
      "val Loss: 0.5487 Acc: 0.7124\n",
      "has spend time 136m 4s/n\n",
      "\n",
      "Epoch 3832/9999\n",
      "----------\n",
      "train Loss: 0.5185 Acc: 0.7336\n",
      "has spend time 136m 5s/n\n",
      "val Loss: 0.5521 Acc: 0.7059\n",
      "has spend time 136m 6s/n\n",
      "\n",
      "Epoch 3833/9999\n",
      "----------\n",
      "train Loss: 0.5065 Acc: 0.7336\n",
      "has spend time 136m 7s/n\n",
      "val Loss: 0.5461 Acc: 0.7190\n",
      "has spend time 136m 8s/n\n",
      "\n",
      "Epoch 3834/9999\n",
      "----------\n",
      "train Loss: 0.5192 Acc: 0.7336\n",
      "has spend time 136m 9s/n\n",
      "val Loss: 0.5443 Acc: 0.6993\n",
      "has spend time 136m 10s/n\n",
      "\n",
      "Epoch 3835/9999\n",
      "----------\n",
      "train Loss: 0.5170 Acc: 0.7295\n",
      "has spend time 136m 11s/n\n",
      "val Loss: 0.5633 Acc: 0.7059\n",
      "has spend time 136m 12s/n\n",
      "\n",
      "Epoch 3836/9999\n",
      "----------\n",
      "train Loss: 0.5374 Acc: 0.7213\n",
      "has spend time 136m 13s/n\n",
      "val Loss: 0.5527 Acc: 0.7059\n",
      "has spend time 136m 14s/n\n",
      "\n",
      "Epoch 3837/9999\n",
      "----------\n",
      "train Loss: 0.5070 Acc: 0.7459\n",
      "has spend time 136m 16s/n\n",
      "val Loss: 0.5701 Acc: 0.6928\n",
      "has spend time 136m 16s/n\n",
      "\n",
      "Epoch 3838/9999\n",
      "----------\n",
      "train Loss: 0.4987 Acc: 0.7377\n",
      "has spend time 136m 18s/n\n",
      "val Loss: 0.5609 Acc: 0.7059\n",
      "has spend time 136m 19s/n\n",
      "\n",
      "Epoch 3839/9999\n",
      "----------\n",
      "train Loss: 0.5250 Acc: 0.7090\n",
      "has spend time 136m 20s/n\n",
      "val Loss: 0.5556 Acc: 0.7059\n",
      "has spend time 136m 21s/n\n",
      "\n",
      "Epoch 3840/9999\n",
      "----------\n",
      "train Loss: 0.5115 Acc: 0.7131\n",
      "has spend time 136m 22s/n\n",
      "val Loss: 0.5627 Acc: 0.7059\n",
      "has spend time 136m 23s/n\n",
      "\n",
      "Epoch 3841/9999\n",
      "----------\n",
      "train Loss: 0.4894 Acc: 0.7459\n",
      "has spend time 136m 24s/n\n",
      "val Loss: 0.5494 Acc: 0.7124\n",
      "has spend time 136m 25s/n\n",
      "\n",
      "Epoch 3842/9999\n",
      "----------\n",
      "train Loss: 0.5107 Acc: 0.7336\n",
      "has spend time 136m 26s/n\n",
      "val Loss: 0.5515 Acc: 0.7190\n",
      "has spend time 136m 27s/n\n",
      "\n",
      "Epoch 3843/9999\n",
      "----------\n",
      "train Loss: 0.5518 Acc: 0.7172\n",
      "has spend time 136m 28s/n\n",
      "val Loss: 0.5495 Acc: 0.7124\n",
      "has spend time 136m 29s/n\n",
      "\n",
      "Epoch 3844/9999\n",
      "----------\n",
      "train Loss: 0.5116 Acc: 0.7090\n",
      "has spend time 136m 30s/n\n",
      "val Loss: 0.5558 Acc: 0.6993\n",
      "has spend time 136m 31s/n\n",
      "\n",
      "Epoch 3845/9999\n",
      "----------\n",
      "train Loss: 0.5259 Acc: 0.6885\n",
      "has spend time 136m 33s/n\n",
      "val Loss: 0.5775 Acc: 0.6928\n",
      "has spend time 136m 33s/n\n",
      "\n",
      "Epoch 3846/9999\n",
      "----------\n",
      "train Loss: 0.4870 Acc: 0.7500\n",
      "has spend time 136m 35s/n\n",
      "val Loss: 0.5540 Acc: 0.7059\n",
      "has spend time 136m 35s/n\n",
      "\n",
      "Epoch 3847/9999\n",
      "----------\n",
      "train Loss: 0.5051 Acc: 0.7336\n",
      "has spend time 136m 37s/n\n",
      "val Loss: 0.5451 Acc: 0.6993\n",
      "has spend time 136m 38s/n\n",
      "\n",
      "Epoch 3848/9999\n",
      "----------\n",
      "train Loss: 0.4944 Acc: 0.7664\n",
      "has spend time 136m 39s/n\n",
      "val Loss: 0.5467 Acc: 0.7124\n",
      "has spend time 136m 40s/n\n",
      "\n",
      "Epoch 3849/9999\n",
      "----------\n",
      "train Loss: 0.5109 Acc: 0.7541\n",
      "has spend time 136m 41s/n\n",
      "val Loss: 0.5463 Acc: 0.7124\n",
      "has spend time 136m 42s/n\n",
      "\n",
      "Epoch 3850/9999\n",
      "----------\n",
      "train Loss: 0.5190 Acc: 0.7459\n",
      "has spend time 136m 43s/n\n",
      "val Loss: 0.5570 Acc: 0.6993\n",
      "has spend time 136m 44s/n\n",
      "\n",
      "Epoch 3851/9999\n",
      "----------\n",
      "train Loss: 0.5148 Acc: 0.7459\n",
      "has spend time 136m 45s/n\n",
      "val Loss: 0.5448 Acc: 0.7124\n",
      "has spend time 136m 46s/n\n",
      "\n",
      "Epoch 3852/9999\n",
      "----------\n",
      "train Loss: 0.5134 Acc: 0.7336\n",
      "has spend time 136m 47s/n\n",
      "val Loss: 0.5528 Acc: 0.6993\n",
      "has spend time 136m 48s/n\n",
      "\n",
      "Epoch 3853/9999\n",
      "----------\n",
      "train Loss: 0.4875 Acc: 0.7541\n",
      "has spend time 136m 49s/n\n",
      "val Loss: 0.5471 Acc: 0.7190\n",
      "has spend time 136m 50s/n\n",
      "\n",
      "Epoch 3854/9999\n",
      "----------\n",
      "train Loss: 0.5001 Acc: 0.7582\n",
      "has spend time 136m 52s/n\n",
      "val Loss: 0.5525 Acc: 0.7124\n",
      "has spend time 136m 53s/n\n",
      "\n",
      "Epoch 3855/9999\n",
      "----------\n",
      "train Loss: 0.4845 Acc: 0.7254\n",
      "has spend time 136m 54s/n\n",
      "val Loss: 0.5538 Acc: 0.7124\n",
      "has spend time 136m 55s/n\n",
      "\n",
      "Epoch 3856/9999\n",
      "----------\n",
      "train Loss: 0.5131 Acc: 0.7377\n",
      "has spend time 136m 56s/n\n",
      "val Loss: 0.5429 Acc: 0.7059\n",
      "has spend time 136m 57s/n\n",
      "\n",
      "Epoch 3857/9999\n",
      "----------\n",
      "train Loss: 0.5142 Acc: 0.7459\n",
      "has spend time 136m 58s/n\n",
      "val Loss: 0.5454 Acc: 0.7124\n",
      "has spend time 136m 59s/n\n",
      "\n",
      "Epoch 3858/9999\n",
      "----------\n",
      "train Loss: 0.5196 Acc: 0.7254\n",
      "has spend time 137m 1s/n\n",
      "val Loss: 0.5464 Acc: 0.7124\n",
      "has spend time 137m 1s/n\n",
      "\n",
      "Epoch 3859/9999\n",
      "----------\n",
      "train Loss: 0.5139 Acc: 0.7377\n",
      "has spend time 137m 3s/n\n",
      "val Loss: 0.5617 Acc: 0.6928\n",
      "has spend time 137m 3s/n\n",
      "\n",
      "Epoch 3860/9999\n",
      "----------\n",
      "train Loss: 0.5224 Acc: 0.7049\n",
      "has spend time 137m 5s/n\n",
      "val Loss: 0.5621 Acc: 0.6928\n",
      "has spend time 137m 5s/n\n",
      "\n",
      "Epoch 3861/9999\n",
      "----------\n",
      "train Loss: 0.5201 Acc: 0.7213\n",
      "has spend time 137m 7s/n\n",
      "val Loss: 0.5584 Acc: 0.6993\n",
      "has spend time 137m 7s/n\n",
      "\n",
      "Epoch 3862/9999\n",
      "----------\n",
      "train Loss: 0.5013 Acc: 0.7254\n",
      "has spend time 137m 9s/n\n",
      "val Loss: 0.5512 Acc: 0.7059\n",
      "has spend time 137m 10s/n\n",
      "\n",
      "Epoch 3863/9999\n",
      "----------\n",
      "train Loss: 0.5007 Acc: 0.7008\n",
      "has spend time 137m 11s/n\n",
      "val Loss: 0.5498 Acc: 0.7059\n",
      "has spend time 137m 12s/n\n",
      "\n",
      "Epoch 3864/9999\n",
      "----------\n",
      "train Loss: 0.5029 Acc: 0.7377\n",
      "has spend time 137m 14s/n\n",
      "val Loss: 0.5508 Acc: 0.7190\n",
      "has spend time 137m 14s/n\n",
      "\n",
      "Epoch 3865/9999\n",
      "----------\n",
      "train Loss: 0.5275 Acc: 0.7541\n",
      "has spend time 137m 16s/n\n",
      "val Loss: 0.5501 Acc: 0.6993\n",
      "has spend time 137m 16s/n\n",
      "\n",
      "Epoch 3866/9999\n",
      "----------\n",
      "train Loss: 0.5144 Acc: 0.7377\n",
      "has spend time 137m 18s/n\n",
      "val Loss: 0.5638 Acc: 0.6993\n",
      "has spend time 137m 18s/n\n",
      "\n",
      "Epoch 3867/9999\n",
      "----------\n",
      "train Loss: 0.5106 Acc: 0.7213\n",
      "has spend time 137m 20s/n\n",
      "val Loss: 0.5689 Acc: 0.6928\n",
      "has spend time 137m 20s/n\n",
      "\n",
      "Epoch 3868/9999\n",
      "----------\n",
      "train Loss: 0.5175 Acc: 0.7295\n",
      "has spend time 137m 22s/n\n",
      "val Loss: 0.5539 Acc: 0.6993\n",
      "has spend time 137m 23s/n\n",
      "\n",
      "Epoch 3869/9999\n",
      "----------\n",
      "train Loss: 0.5412 Acc: 0.6926\n",
      "has spend time 137m 24s/n\n",
      "val Loss: 0.5462 Acc: 0.7190\n",
      "has spend time 137m 25s/n\n",
      "\n",
      "Epoch 3870/9999\n",
      "----------\n",
      "train Loss: 0.5122 Acc: 0.7172\n",
      "has spend time 137m 26s/n\n",
      "val Loss: 0.5502 Acc: 0.7124\n",
      "has spend time 137m 27s/n\n",
      "\n",
      "Epoch 3871/9999\n",
      "----------\n",
      "train Loss: 0.5351 Acc: 0.7172\n",
      "has spend time 137m 28s/n\n",
      "val Loss: 0.5574 Acc: 0.7124\n",
      "has spend time 137m 29s/n\n",
      "\n",
      "Epoch 3872/9999\n",
      "----------\n",
      "train Loss: 0.5112 Acc: 0.7500\n",
      "has spend time 137m 30s/n\n",
      "val Loss: 0.5475 Acc: 0.7124\n",
      "has spend time 137m 31s/n\n",
      "\n",
      "Epoch 3873/9999\n",
      "----------\n",
      "train Loss: 0.5002 Acc: 0.7418\n",
      "has spend time 137m 32s/n\n",
      "val Loss: 0.5398 Acc: 0.7190\n",
      "has spend time 137m 33s/n\n",
      "\n",
      "Epoch 3874/9999\n",
      "----------\n",
      "train Loss: 0.5614 Acc: 0.6967\n",
      "has spend time 137m 35s/n\n",
      "val Loss: 0.5594 Acc: 0.6993\n",
      "has spend time 137m 35s/n\n",
      "\n",
      "Epoch 3875/9999\n",
      "----------\n",
      "train Loss: 0.4860 Acc: 0.7459\n",
      "has spend time 137m 37s/n\n",
      "val Loss: 0.5565 Acc: 0.6993\n",
      "has spend time 137m 37s/n\n",
      "\n",
      "Epoch 3876/9999\n",
      "----------\n",
      "train Loss: 0.5198 Acc: 0.7295\n",
      "has spend time 137m 39s/n\n",
      "val Loss: 0.5473 Acc: 0.6993\n",
      "has spend time 137m 40s/n\n",
      "\n",
      "Epoch 3877/9999\n",
      "----------\n",
      "train Loss: 0.4926 Acc: 0.7459\n",
      "has spend time 137m 41s/n\n",
      "val Loss: 0.5462 Acc: 0.7124\n",
      "has spend time 137m 42s/n\n",
      "\n",
      "Epoch 3878/9999\n",
      "----------\n",
      "train Loss: 0.4982 Acc: 0.7541\n",
      "has spend time 137m 43s/n\n",
      "val Loss: 0.5457 Acc: 0.7059\n",
      "has spend time 137m 44s/n\n",
      "\n",
      "Epoch 3879/9999\n",
      "----------\n",
      "train Loss: 0.5311 Acc: 0.7418\n",
      "has spend time 137m 45s/n\n",
      "val Loss: 0.5600 Acc: 0.6928\n",
      "has spend time 137m 46s/n\n",
      "\n",
      "Epoch 3880/9999\n",
      "----------\n",
      "train Loss: 0.4889 Acc: 0.7664\n",
      "has spend time 137m 47s/n\n",
      "val Loss: 0.5493 Acc: 0.7124\n",
      "has spend time 137m 48s/n\n",
      "\n",
      "Epoch 3881/9999\n",
      "----------\n",
      "train Loss: 0.5076 Acc: 0.7254\n",
      "has spend time 137m 49s/n\n",
      "val Loss: 0.5787 Acc: 0.6993\n",
      "has spend time 137m 50s/n\n",
      "\n",
      "Epoch 3882/9999\n",
      "----------\n",
      "train Loss: 0.5018 Acc: 0.7336\n",
      "has spend time 137m 52s/n\n",
      "val Loss: 0.5605 Acc: 0.7059\n",
      "has spend time 137m 53s/n\n",
      "\n",
      "Epoch 3883/9999\n",
      "----------\n",
      "train Loss: 0.4827 Acc: 0.7623\n",
      "has spend time 137m 54s/n\n",
      "val Loss: 0.5540 Acc: 0.7124\n",
      "has spend time 137m 55s/n\n",
      "\n",
      "Epoch 3884/9999\n",
      "----------\n",
      "train Loss: 0.4950 Acc: 0.7459\n",
      "has spend time 137m 56s/n\n",
      "val Loss: 0.5618 Acc: 0.7059\n",
      "has spend time 137m 57s/n\n",
      "\n",
      "Epoch 3885/9999\n",
      "----------\n",
      "train Loss: 0.5128 Acc: 0.7213\n",
      "has spend time 137m 58s/n\n",
      "val Loss: 0.5686 Acc: 0.6928\n",
      "has spend time 137m 59s/n\n",
      "\n",
      "Epoch 3886/9999\n",
      "----------\n",
      "train Loss: 0.5088 Acc: 0.7172\n",
      "has spend time 138m 0s/n\n",
      "val Loss: 0.5503 Acc: 0.7124\n",
      "has spend time 138m 1s/n\n",
      "\n",
      "Epoch 3887/9999\n",
      "----------\n",
      "train Loss: 0.4979 Acc: 0.7377\n",
      "has spend time 138m 2s/n\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val Loss: 0.5515 Acc: 0.7190\n",
      "has spend time 138m 3s/n\n",
      "\n",
      "Epoch 3888/9999\n",
      "----------\n",
      "train Loss: 0.5332 Acc: 0.7459\n",
      "has spend time 138m 5s/n\n",
      "val Loss: 0.5466 Acc: 0.7190\n",
      "has spend time 138m 5s/n\n",
      "\n",
      "Epoch 3889/9999\n",
      "----------\n",
      "train Loss: 0.4898 Acc: 0.7705\n",
      "has spend time 138m 7s/n\n",
      "val Loss: 0.5592 Acc: 0.6993\n",
      "has spend time 138m 7s/n\n",
      "\n",
      "Epoch 3890/9999\n",
      "----------\n",
      "train Loss: 0.4727 Acc: 0.8033\n",
      "has spend time 138m 9s/n\n",
      "val Loss: 0.5516 Acc: 0.7059\n",
      "has spend time 138m 10s/n\n",
      "\n",
      "Epoch 3891/9999\n",
      "----------\n",
      "train Loss: 0.5024 Acc: 0.7418\n",
      "has spend time 138m 11s/n\n",
      "val Loss: 0.5490 Acc: 0.7124\n",
      "has spend time 138m 12s/n\n",
      "\n",
      "Epoch 3892/9999\n",
      "----------\n",
      "train Loss: 0.5417 Acc: 0.7254\n",
      "has spend time 138m 13s/n\n",
      "val Loss: 0.5464 Acc: 0.7190\n",
      "has spend time 138m 14s/n\n",
      "\n",
      "Epoch 3893/9999\n",
      "----------\n",
      "train Loss: 0.4965 Acc: 0.7664\n",
      "has spend time 138m 15s/n\n",
      "val Loss: 0.5489 Acc: 0.7190\n",
      "has spend time 138m 16s/n\n",
      "\n",
      "Epoch 3894/9999\n",
      "----------\n",
      "train Loss: 0.5246 Acc: 0.7295\n",
      "has spend time 138m 17s/n\n",
      "val Loss: 0.5463 Acc: 0.7124\n",
      "has spend time 138m 18s/n\n",
      "\n",
      "Epoch 3895/9999\n",
      "----------\n",
      "train Loss: 0.4932 Acc: 0.7746\n",
      "has spend time 138m 19s/n\n",
      "val Loss: 0.5472 Acc: 0.7059\n",
      "has spend time 138m 20s/n\n",
      "\n",
      "Epoch 3896/9999\n",
      "----------\n",
      "train Loss: 0.4981 Acc: 0.7705\n",
      "has spend time 138m 21s/n\n",
      "val Loss: 0.5490 Acc: 0.7059\n",
      "has spend time 138m 22s/n\n",
      "\n",
      "Epoch 3897/9999\n",
      "----------\n",
      "train Loss: 0.4725 Acc: 0.7787\n",
      "has spend time 138m 24s/n\n",
      "val Loss: 0.5492 Acc: 0.7190\n",
      "has spend time 138m 24s/n\n",
      "\n",
      "Epoch 3898/9999\n",
      "----------\n",
      "train Loss: 0.4817 Acc: 0.7418\n",
      "has spend time 138m 26s/n\n",
      "val Loss: 0.5475 Acc: 0.7059\n",
      "has spend time 138m 26s/n\n",
      "\n",
      "Epoch 3899/9999\n",
      "----------\n",
      "train Loss: 0.4734 Acc: 0.7582\n",
      "has spend time 138m 28s/n\n",
      "val Loss: 0.5498 Acc: 0.7124\n",
      "has spend time 138m 28s/n\n",
      "\n",
      "Epoch 3900/9999\n",
      "----------\n",
      "train Loss: 0.5049 Acc: 0.7377\n",
      "has spend time 138m 30s/n\n",
      "val Loss: 0.5507 Acc: 0.6993\n",
      "has spend time 138m 30s/n\n",
      "\n",
      "Epoch 3901/9999\n",
      "----------\n",
      "train Loss: 0.4883 Acc: 0.7582\n",
      "has spend time 138m 32s/n\n",
      "val Loss: 0.5525 Acc: 0.7059\n",
      "has spend time 138m 32s/n\n",
      "\n",
      "Epoch 3902/9999\n",
      "----------\n",
      "train Loss: 0.5120 Acc: 0.7623\n",
      "has spend time 138m 34s/n\n",
      "val Loss: 0.5669 Acc: 0.6928\n",
      "has spend time 138m 34s/n\n",
      "\n",
      "Epoch 3903/9999\n",
      "----------\n",
      "train Loss: 0.4904 Acc: 0.7664\n",
      "has spend time 138m 36s/n\n",
      "val Loss: 0.5604 Acc: 0.7059\n",
      "has spend time 138m 36s/n\n",
      "\n",
      "Epoch 3904/9999\n",
      "----------\n",
      "train Loss: 0.4878 Acc: 0.7213\n",
      "has spend time 138m 38s/n\n",
      "val Loss: 0.5439 Acc: 0.7124\n",
      "has spend time 138m 39s/n\n",
      "\n",
      "Epoch 3905/9999\n",
      "----------\n",
      "train Loss: 0.5147 Acc: 0.7664\n",
      "has spend time 138m 40s/n\n",
      "val Loss: 0.5654 Acc: 0.7059\n",
      "has spend time 138m 41s/n\n",
      "\n",
      "Epoch 3906/9999\n",
      "----------\n",
      "train Loss: 0.5393 Acc: 0.7336\n",
      "has spend time 138m 43s/n\n",
      "val Loss: 0.5443 Acc: 0.7124\n",
      "has spend time 138m 43s/n\n",
      "\n",
      "Epoch 3907/9999\n",
      "----------\n",
      "train Loss: 0.5119 Acc: 0.7418\n",
      "has spend time 138m 45s/n\n",
      "val Loss: 0.5420 Acc: 0.7255\n",
      "has spend time 138m 45s/n\n",
      "\n",
      "Epoch 3908/9999\n",
      "----------\n",
      "train Loss: 0.4999 Acc: 0.7254\n",
      "has spend time 138m 47s/n\n",
      "val Loss: 0.5450 Acc: 0.7124\n",
      "has spend time 138m 47s/n\n",
      "\n",
      "Epoch 3909/9999\n",
      "----------\n",
      "train Loss: 0.5115 Acc: 0.7623\n",
      "has spend time 138m 49s/n\n",
      "val Loss: 0.5465 Acc: 0.7124\n",
      "has spend time 138m 50s/n\n",
      "\n",
      "Epoch 3910/9999\n",
      "----------\n",
      "train Loss: 0.4810 Acc: 0.7295\n",
      "has spend time 138m 51s/n\n",
      "val Loss: 0.5446 Acc: 0.7190\n",
      "has spend time 138m 52s/n\n",
      "\n",
      "Epoch 3911/9999\n",
      "----------\n",
      "train Loss: 0.5119 Acc: 0.7500\n",
      "has spend time 138m 53s/n\n",
      "val Loss: 0.5510 Acc: 0.7124\n",
      "has spend time 138m 54s/n\n",
      "\n",
      "Epoch 3912/9999\n",
      "----------\n",
      "train Loss: 0.4966 Acc: 0.7459\n",
      "has spend time 138m 55s/n\n",
      "val Loss: 0.5486 Acc: 0.7124\n",
      "has spend time 138m 56s/n\n",
      "\n",
      "Epoch 3913/9999\n",
      "----------\n",
      "train Loss: 0.5361 Acc: 0.7336\n",
      "has spend time 138m 58s/n\n",
      "val Loss: 0.5443 Acc: 0.7190\n",
      "has spend time 138m 58s/n\n",
      "\n",
      "Epoch 3914/9999\n",
      "----------\n",
      "train Loss: 0.5110 Acc: 0.7459\n",
      "has spend time 138m 60s/n\n",
      "val Loss: 0.5442 Acc: 0.7320\n",
      "has spend time 139m 0s/n\n",
      "\n",
      "Epoch 3915/9999\n",
      "----------\n",
      "train Loss: 0.5327 Acc: 0.7090\n",
      "has spend time 139m 2s/n\n",
      "val Loss: 0.5459 Acc: 0.7124\n",
      "has spend time 139m 2s/n\n",
      "\n",
      "Epoch 3916/9999\n",
      "----------\n",
      "train Loss: 0.4993 Acc: 0.7787\n",
      "has spend time 139m 4s/n\n",
      "val Loss: 0.5449 Acc: 0.7190\n",
      "has spend time 139m 4s/n\n",
      "\n",
      "Epoch 3917/9999\n",
      "----------\n",
      "train Loss: 0.5145 Acc: 0.7254\n",
      "has spend time 139m 6s/n\n",
      "val Loss: 0.5503 Acc: 0.7059\n",
      "has spend time 139m 6s/n\n",
      "\n",
      "Epoch 3918/9999\n",
      "----------\n",
      "train Loss: 0.5147 Acc: 0.7295\n",
      "has spend time 139m 8s/n\n",
      "val Loss: 0.5508 Acc: 0.7059\n",
      "has spend time 139m 8s/n\n",
      "\n",
      "Epoch 3919/9999\n",
      "----------\n",
      "train Loss: 0.4689 Acc: 0.7582\n",
      "has spend time 139m 10s/n\n",
      "val Loss: 0.5551 Acc: 0.7124\n",
      "has spend time 139m 11s/n\n",
      "\n",
      "Epoch 3920/9999\n",
      "----------\n",
      "train Loss: 0.5015 Acc: 0.7172\n",
      "has spend time 139m 12s/n\n",
      "val Loss: 0.5594 Acc: 0.6993\n",
      "has spend time 139m 13s/n\n",
      "\n",
      "Epoch 3921/9999\n",
      "----------\n",
      "train Loss: 0.4813 Acc: 0.7787\n",
      "has spend time 139m 14s/n\n",
      "val Loss: 0.5677 Acc: 0.6928\n",
      "has spend time 139m 15s/n\n",
      "\n",
      "Epoch 3922/9999\n",
      "----------\n",
      "train Loss: 0.5248 Acc: 0.7172\n",
      "has spend time 139m 16s/n\n",
      "val Loss: 0.5551 Acc: 0.7059\n",
      "has spend time 139m 17s/n\n",
      "\n",
      "Epoch 3923/9999\n",
      "----------\n",
      "train Loss: 0.5231 Acc: 0.7090\n",
      "has spend time 139m 18s/n\n",
      "val Loss: 0.5427 Acc: 0.7190\n",
      "has spend time 139m 19s/n\n",
      "\n",
      "Epoch 3924/9999\n",
      "----------\n",
      "train Loss: 0.5125 Acc: 0.7295\n",
      "has spend time 139m 20s/n\n",
      "val Loss: 0.5510 Acc: 0.6993\n",
      "has spend time 139m 21s/n\n",
      "\n",
      "Epoch 3925/9999\n",
      "----------\n",
      "train Loss: 0.5064 Acc: 0.7377\n",
      "has spend time 139m 22s/n\n",
      "val Loss: 0.5586 Acc: 0.7059\n",
      "has spend time 139m 23s/n\n",
      "\n",
      "Epoch 3926/9999\n",
      "----------\n",
      "train Loss: 0.4987 Acc: 0.7500\n",
      "has spend time 139m 25s/n\n",
      "val Loss: 0.5619 Acc: 0.7059\n",
      "has spend time 139m 25s/n\n",
      "\n",
      "Epoch 3927/9999\n",
      "----------\n",
      "train Loss: 0.5230 Acc: 0.7418\n",
      "has spend time 139m 27s/n\n",
      "val Loss: 0.5449 Acc: 0.7190\n",
      "has spend time 139m 28s/n\n",
      "\n",
      "Epoch 3928/9999\n",
      "----------\n",
      "train Loss: 0.5067 Acc: 0.7336\n",
      "has spend time 139m 29s/n\n",
      "val Loss: 0.5425 Acc: 0.7059\n",
      "has spend time 139m 30s/n\n",
      "\n",
      "Epoch 3929/9999\n",
      "----------\n",
      "train Loss: 0.5010 Acc: 0.7582\n",
      "has spend time 139m 31s/n\n",
      "val Loss: 0.5533 Acc: 0.7124\n",
      "has spend time 139m 32s/n\n",
      "\n",
      "Epoch 3930/9999\n",
      "----------\n",
      "train Loss: 0.5417 Acc: 0.7254\n",
      "has spend time 139m 33s/n\n",
      "val Loss: 0.5465 Acc: 0.7059\n",
      "has spend time 139m 34s/n\n",
      "\n",
      "Epoch 3931/9999\n",
      "----------\n",
      "train Loss: 0.4844 Acc: 0.7295\n",
      "has spend time 139m 35s/n\n",
      "val Loss: 0.5584 Acc: 0.6928\n",
      "has spend time 139m 36s/n\n",
      "\n",
      "Epoch 3932/9999\n",
      "----------\n",
      "train Loss: 0.5057 Acc: 0.7377\n",
      "has spend time 139m 37s/n\n",
      "val Loss: 0.5557 Acc: 0.7059\n",
      "has spend time 139m 38s/n\n",
      "\n",
      "Epoch 3933/9999\n",
      "----------\n",
      "train Loss: 0.5054 Acc: 0.7500\n",
      "has spend time 139m 40s/n\n",
      "val Loss: 0.5428 Acc: 0.7190\n",
      "has spend time 139m 40s/n\n",
      "\n",
      "Epoch 3934/9999\n",
      "----------\n",
      "train Loss: 0.5119 Acc: 0.7213\n",
      "has spend time 139m 42s/n\n",
      "val Loss: 0.5502 Acc: 0.7059\n",
      "has spend time 139m 42s/n\n",
      "\n",
      "Epoch 3935/9999\n",
      "----------\n",
      "train Loss: 0.5049 Acc: 0.7582\n",
      "has spend time 139m 44s/n\n",
      "val Loss: 0.5781 Acc: 0.6993\n",
      "has spend time 139m 44s/n\n",
      "\n",
      "Epoch 3936/9999\n",
      "----------\n",
      "train Loss: 0.4993 Acc: 0.7541\n",
      "has spend time 139m 46s/n\n",
      "val Loss: 0.5610 Acc: 0.6993\n",
      "has spend time 139m 46s/n\n",
      "\n",
      "Epoch 3937/9999\n",
      "----------\n",
      "train Loss: 0.5002 Acc: 0.7377\n",
      "has spend time 139m 48s/n\n",
      "val Loss: 0.5506 Acc: 0.6993\n",
      "has spend time 139m 48s/n\n",
      "\n",
      "Epoch 3938/9999\n",
      "----------\n",
      "train Loss: 0.5275 Acc: 0.7254\n",
      "has spend time 139m 50s/n\n",
      "val Loss: 0.5724 Acc: 0.6928\n",
      "has spend time 139m 50s/n\n",
      "\n",
      "Epoch 3939/9999\n",
      "----------\n",
      "train Loss: 0.5109 Acc: 0.7131\n",
      "has spend time 139m 52s/n\n",
      "val Loss: 0.5664 Acc: 0.6928\n",
      "has spend time 139m 53s/n\n",
      "\n",
      "Epoch 3940/9999\n",
      "----------\n",
      "train Loss: 0.5346 Acc: 0.7500\n",
      "has spend time 139m 54s/n\n",
      "val Loss: 0.5597 Acc: 0.6993\n",
      "has spend time 139m 55s/n\n",
      "\n",
      "Epoch 3941/9999\n",
      "----------\n",
      "train Loss: 0.5115 Acc: 0.7213\n",
      "has spend time 139m 56s/n\n",
      "val Loss: 0.5481 Acc: 0.7124\n",
      "has spend time 139m 57s/n\n",
      "\n",
      "Epoch 3942/9999\n",
      "----------\n",
      "train Loss: 0.4980 Acc: 0.7541\n",
      "has spend time 139m 58s/n\n",
      "val Loss: 0.5645 Acc: 0.6993\n",
      "has spend time 139m 59s/n\n",
      "\n",
      "Epoch 3943/9999\n",
      "----------\n",
      "train Loss: 0.5414 Acc: 0.7213\n",
      "has spend time 140m 0s/n\n",
      "val Loss: 0.5606 Acc: 0.7059\n",
      "has spend time 140m 1s/n\n",
      "\n",
      "Epoch 3944/9999\n",
      "----------\n",
      "train Loss: 0.5191 Acc: 0.7295\n",
      "has spend time 140m 2s/n\n",
      "val Loss: 0.5443 Acc: 0.7255\n",
      "has spend time 140m 3s/n\n",
      "\n",
      "Epoch 3945/9999\n",
      "----------\n",
      "train Loss: 0.5212 Acc: 0.7254\n",
      "has spend time 140m 4s/n\n",
      "val Loss: 0.5511 Acc: 0.7124\n",
      "has spend time 140m 5s/n\n",
      "\n",
      "Epoch 3946/9999\n",
      "----------\n",
      "train Loss: 0.4947 Acc: 0.7664\n",
      "has spend time 140m 6s/n\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val Loss: 0.5456 Acc: 0.7124\n",
      "has spend time 140m 7s/n\n",
      "\n",
      "Epoch 3947/9999\n",
      "----------\n",
      "train Loss: 0.4927 Acc: 0.7664\n",
      "has spend time 140m 8s/n\n",
      "val Loss: 0.5538 Acc: 0.6928\n",
      "has spend time 140m 9s/n\n",
      "\n",
      "Epoch 3948/9999\n",
      "----------\n",
      "train Loss: 0.4787 Acc: 0.7746\n",
      "has spend time 140m 10s/n\n",
      "val Loss: 0.5516 Acc: 0.7059\n",
      "has spend time 140m 11s/n\n",
      "\n",
      "Epoch 3949/9999\n",
      "----------\n",
      "train Loss: 0.4814 Acc: 0.7828\n",
      "has spend time 140m 13s/n\n",
      "val Loss: 0.5428 Acc: 0.7190\n",
      "has spend time 140m 13s/n\n",
      "\n",
      "Epoch 3950/9999\n",
      "----------\n",
      "train Loss: 0.5128 Acc: 0.7459\n",
      "has spend time 140m 15s/n\n",
      "val Loss: 0.5506 Acc: 0.7059\n",
      "has spend time 140m 15s/n\n",
      "\n",
      "Epoch 3951/9999\n",
      "----------\n",
      "train Loss: 0.5191 Acc: 0.7459\n",
      "has spend time 140m 17s/n\n",
      "val Loss: 0.5407 Acc: 0.7190\n",
      "has spend time 140m 17s/n\n",
      "\n",
      "Epoch 3952/9999\n",
      "----------\n",
      "train Loss: 0.5010 Acc: 0.7705\n",
      "has spend time 140m 19s/n\n",
      "val Loss: 0.5448 Acc: 0.7255\n",
      "has spend time 140m 19s/n\n",
      "\n",
      "Epoch 3953/9999\n",
      "----------\n",
      "train Loss: 0.4969 Acc: 0.7254\n",
      "has spend time 140m 21s/n\n",
      "val Loss: 0.5469 Acc: 0.7255\n",
      "has spend time 140m 21s/n\n",
      "\n",
      "Epoch 3954/9999\n",
      "----------\n",
      "train Loss: 0.5135 Acc: 0.7541\n",
      "has spend time 140m 23s/n\n",
      "val Loss: 0.5672 Acc: 0.6993\n",
      "has spend time 140m 23s/n\n",
      "\n",
      "Epoch 3955/9999\n",
      "----------\n",
      "train Loss: 0.5128 Acc: 0.7377\n",
      "has spend time 140m 25s/n\n",
      "val Loss: 0.5551 Acc: 0.7124\n",
      "has spend time 140m 25s/n\n",
      "\n",
      "Epoch 3956/9999\n",
      "----------\n",
      "train Loss: 0.5060 Acc: 0.7336\n",
      "has spend time 140m 27s/n\n",
      "val Loss: 0.5564 Acc: 0.7059\n",
      "has spend time 140m 27s/n\n",
      "\n",
      "Epoch 3957/9999\n",
      "----------\n",
      "train Loss: 0.5168 Acc: 0.7295\n",
      "has spend time 140m 29s/n\n",
      "val Loss: 0.5468 Acc: 0.7059\n",
      "has spend time 140m 30s/n\n",
      "\n",
      "Epoch 3958/9999\n",
      "----------\n",
      "train Loss: 0.5166 Acc: 0.7213\n",
      "has spend time 140m 31s/n\n",
      "val Loss: 0.5364 Acc: 0.7255\n",
      "has spend time 140m 32s/n\n",
      "\n",
      "Epoch 3959/9999\n",
      "----------\n",
      "train Loss: 0.5134 Acc: 0.7336\n",
      "has spend time 140m 34s/n\n",
      "val Loss: 0.5529 Acc: 0.7190\n",
      "has spend time 140m 34s/n\n",
      "\n",
      "Epoch 3960/9999\n",
      "----------\n",
      "train Loss: 0.4899 Acc: 0.7336\n",
      "has spend time 140m 36s/n\n",
      "val Loss: 0.5528 Acc: 0.6928\n",
      "has spend time 140m 36s/n\n",
      "\n",
      "Epoch 3961/9999\n",
      "----------\n",
      "train Loss: 0.4958 Acc: 0.7623\n",
      "has spend time 140m 38s/n\n",
      "val Loss: 0.5506 Acc: 0.7124\n",
      "has spend time 140m 38s/n\n",
      "\n",
      "Epoch 3962/9999\n",
      "----------\n",
      "train Loss: 0.5041 Acc: 0.7500\n",
      "has spend time 140m 40s/n\n",
      "val Loss: 0.5431 Acc: 0.7190\n",
      "has spend time 140m 40s/n\n",
      "\n",
      "Epoch 3963/9999\n",
      "----------\n",
      "train Loss: 0.5011 Acc: 0.7500\n",
      "has spend time 140m 42s/n\n",
      "val Loss: 0.5372 Acc: 0.7190\n",
      "has spend time 140m 43s/n\n",
      "\n",
      "Epoch 3964/9999\n",
      "----------\n",
      "train Loss: 0.5318 Acc: 0.7090\n",
      "has spend time 140m 44s/n\n",
      "val Loss: 0.5486 Acc: 0.6993\n",
      "has spend time 140m 45s/n\n",
      "\n",
      "Epoch 3965/9999\n",
      "----------\n",
      "train Loss: 0.4964 Acc: 0.7459\n",
      "has spend time 140m 46s/n\n",
      "val Loss: 0.5433 Acc: 0.7190\n",
      "has spend time 140m 47s/n\n",
      "\n",
      "Epoch 3966/9999\n",
      "----------\n",
      "train Loss: 0.5236 Acc: 0.7459\n",
      "has spend time 140m 48s/n\n",
      "val Loss: 0.5660 Acc: 0.6863\n",
      "has spend time 140m 49s/n\n",
      "\n",
      "Epoch 3967/9999\n",
      "----------\n",
      "train Loss: 0.5143 Acc: 0.7500\n",
      "has spend time 140m 50s/n\n",
      "val Loss: 0.5403 Acc: 0.7124\n",
      "has spend time 140m 51s/n\n",
      "\n",
      "Epoch 3968/9999\n",
      "----------\n",
      "train Loss: 0.5035 Acc: 0.7418\n",
      "has spend time 140m 52s/n\n",
      "val Loss: 0.5490 Acc: 0.6993\n",
      "has spend time 140m 53s/n\n",
      "\n",
      "Epoch 3969/9999\n",
      "----------\n",
      "train Loss: 0.5054 Acc: 0.7418\n",
      "has spend time 140m 54s/n\n",
      "val Loss: 0.5466 Acc: 0.7190\n",
      "has spend time 140m 55s/n\n",
      "\n",
      "Epoch 3970/9999\n",
      "----------\n",
      "train Loss: 0.4648 Acc: 0.7869\n",
      "has spend time 140m 56s/n\n",
      "val Loss: 0.5582 Acc: 0.6993\n",
      "has spend time 140m 57s/n\n",
      "\n",
      "Epoch 3971/9999\n",
      "----------\n",
      "train Loss: 0.5215 Acc: 0.7213\n",
      "has spend time 140m 59s/n\n",
      "val Loss: 0.5498 Acc: 0.6993\n",
      "has spend time 140m 59s/n\n",
      "\n",
      "Epoch 3972/9999\n",
      "----------\n",
      "train Loss: 0.5166 Acc: 0.7418\n",
      "has spend time 141m 1s/n\n",
      "val Loss: 0.5506 Acc: 0.7059\n",
      "has spend time 141m 1s/n\n",
      "\n",
      "Epoch 3973/9999\n",
      "----------\n",
      "train Loss: 0.5165 Acc: 0.7131\n",
      "has spend time 141m 3s/n\n",
      "val Loss: 0.5555 Acc: 0.7124\n",
      "has spend time 141m 4s/n\n",
      "\n",
      "Epoch 3974/9999\n",
      "----------\n",
      "train Loss: 0.5181 Acc: 0.7541\n",
      "has spend time 141m 5s/n\n",
      "val Loss: 0.5630 Acc: 0.6993\n",
      "has spend time 141m 6s/n\n",
      "\n",
      "Epoch 3975/9999\n",
      "----------\n",
      "train Loss: 0.5056 Acc: 0.7418\n",
      "has spend time 141m 8s/n\n",
      "val Loss: 0.5560 Acc: 0.6993\n",
      "has spend time 141m 9s/n\n",
      "\n",
      "Epoch 3976/9999\n",
      "----------\n",
      "train Loss: 0.4944 Acc: 0.7377\n",
      "has spend time 141m 10s/n\n",
      "val Loss: 0.5504 Acc: 0.7059\n",
      "has spend time 141m 11s/n\n",
      "\n",
      "Epoch 3977/9999\n",
      "----------\n",
      "train Loss: 0.4930 Acc: 0.7582\n",
      "has spend time 141m 12s/n\n",
      "val Loss: 0.5623 Acc: 0.6863\n",
      "has spend time 141m 13s/n\n",
      "\n",
      "Epoch 3978/9999\n",
      "----------\n",
      "train Loss: 0.5069 Acc: 0.7254\n",
      "has spend time 141m 14s/n\n",
      "val Loss: 0.5507 Acc: 0.7059\n",
      "has spend time 141m 15s/n\n",
      "\n",
      "Epoch 3979/9999\n",
      "----------\n",
      "train Loss: 0.5113 Acc: 0.7500\n",
      "has spend time 141m 16s/n\n",
      "val Loss: 0.5370 Acc: 0.7255\n",
      "has spend time 141m 17s/n\n",
      "\n",
      "Epoch 3980/9999\n",
      "----------\n",
      "train Loss: 0.5257 Acc: 0.7336\n",
      "has spend time 141m 19s/n\n",
      "val Loss: 0.5552 Acc: 0.6928\n",
      "has spend time 141m 19s/n\n",
      "\n",
      "Epoch 3981/9999\n",
      "----------\n",
      "train Loss: 0.5065 Acc: 0.7705\n",
      "has spend time 141m 21s/n\n",
      "val Loss: 0.5503 Acc: 0.7059\n",
      "has spend time 141m 21s/n\n",
      "\n",
      "Epoch 3982/9999\n",
      "----------\n",
      "train Loss: 0.5259 Acc: 0.7377\n",
      "has spend time 141m 23s/n\n",
      "val Loss: 0.5431 Acc: 0.7190\n",
      "has spend time 141m 23s/n\n",
      "\n",
      "Epoch 3983/9999\n",
      "----------\n",
      "train Loss: 0.4845 Acc: 0.7582\n",
      "has spend time 141m 25s/n\n",
      "val Loss: 0.5483 Acc: 0.7190\n",
      "has spend time 141m 25s/n\n",
      "\n",
      "Epoch 3984/9999\n",
      "----------\n",
      "train Loss: 0.5267 Acc: 0.7295\n",
      "has spend time 141m 27s/n\n",
      "val Loss: 0.5748 Acc: 0.7059\n",
      "has spend time 141m 28s/n\n",
      "\n",
      "Epoch 3985/9999\n",
      "----------\n",
      "train Loss: 0.4987 Acc: 0.7459\n",
      "has spend time 141m 29s/n\n",
      "val Loss: 0.5762 Acc: 0.6993\n",
      "has spend time 141m 30s/n\n",
      "\n",
      "Epoch 3986/9999\n",
      "----------\n",
      "train Loss: 0.4841 Acc: 0.7500\n",
      "has spend time 141m 31s/n\n",
      "val Loss: 0.5564 Acc: 0.7059\n",
      "has spend time 141m 32s/n\n",
      "\n",
      "Epoch 3987/9999\n",
      "----------\n",
      "train Loss: 0.4899 Acc: 0.7582\n",
      "has spend time 141m 34s/n\n",
      "val Loss: 0.5418 Acc: 0.7124\n",
      "has spend time 141m 34s/n\n",
      "\n",
      "Epoch 3988/9999\n",
      "----------\n",
      "train Loss: 0.4989 Acc: 0.7459\n",
      "has spend time 141m 36s/n\n",
      "val Loss: 0.5450 Acc: 0.7124\n",
      "has spend time 141m 36s/n\n",
      "\n",
      "Epoch 3989/9999\n",
      "----------\n",
      "train Loss: 0.5397 Acc: 0.7131\n",
      "has spend time 141m 38s/n\n",
      "val Loss: 0.5561 Acc: 0.7059\n",
      "has spend time 141m 39s/n\n",
      "\n",
      "Epoch 3990/9999\n",
      "----------\n",
      "train Loss: 0.4711 Acc: 0.7623\n",
      "has spend time 141m 40s/n\n",
      "val Loss: 0.5417 Acc: 0.7255\n",
      "has spend time 141m 41s/n\n",
      "\n",
      "Epoch 3991/9999\n",
      "----------\n",
      "train Loss: 0.5366 Acc: 0.7213\n",
      "has spend time 141m 42s/n\n",
      "val Loss: 0.5460 Acc: 0.7124\n",
      "has spend time 141m 43s/n\n",
      "\n",
      "Epoch 3992/9999\n",
      "----------\n",
      "train Loss: 0.5669 Acc: 0.6680\n",
      "has spend time 141m 44s/n\n",
      "val Loss: 0.5719 Acc: 0.7059\n",
      "has spend time 141m 45s/n\n",
      "\n",
      "Epoch 3993/9999\n",
      "----------\n",
      "train Loss: 0.5413 Acc: 0.7254\n",
      "has spend time 141m 47s/n\n",
      "val Loss: 0.5509 Acc: 0.6993\n",
      "has spend time 141m 47s/n\n",
      "\n",
      "Epoch 3994/9999\n",
      "----------\n",
      "train Loss: 0.5140 Acc: 0.7500\n",
      "has spend time 141m 49s/n\n",
      "val Loss: 0.5548 Acc: 0.7124\n",
      "has spend time 141m 50s/n\n",
      "\n",
      "Epoch 3995/9999\n",
      "----------\n",
      "train Loss: 0.4925 Acc: 0.7705\n",
      "has spend time 141m 51s/n\n",
      "val Loss: 0.5553 Acc: 0.6928\n",
      "has spend time 141m 52s/n\n",
      "\n",
      "Epoch 3996/9999\n",
      "----------\n",
      "train Loss: 0.5060 Acc: 0.7418\n",
      "has spend time 141m 53s/n\n",
      "val Loss: 0.5445 Acc: 0.7124\n",
      "has spend time 141m 54s/n\n",
      "\n",
      "Epoch 3997/9999\n",
      "----------\n",
      "train Loss: 0.5336 Acc: 0.7008\n",
      "has spend time 141m 55s/n\n",
      "val Loss: 0.5499 Acc: 0.7190\n",
      "has spend time 141m 56s/n\n",
      "\n",
      "Epoch 3998/9999\n",
      "----------\n",
      "train Loss: 0.5282 Acc: 0.7336\n",
      "has spend time 141m 57s/n\n",
      "val Loss: 0.5595 Acc: 0.6928\n",
      "has spend time 141m 58s/n\n",
      "\n",
      "Epoch 3999/9999\n",
      "----------\n",
      "train Loss: 0.5179 Acc: 0.7459\n",
      "has spend time 141m 59s/n\n",
      "val Loss: 0.5525 Acc: 0.6993\n",
      "has spend time 142m 0s/n\n",
      "\n",
      "Epoch 4000/9999\n",
      "----------\n",
      "train Loss: 0.4919 Acc: 0.7459\n",
      "has spend time 142m 1s/n\n",
      "val Loss: 0.5432 Acc: 0.7124\n",
      "has spend time 142m 2s/n\n",
      "\n",
      "Epoch 4001/9999\n",
      "----------\n",
      "train Loss: 0.4963 Acc: 0.7336\n",
      "has spend time 142m 4s/n\n",
      "val Loss: 0.5407 Acc: 0.7320\n",
      "has spend time 142m 4s/n\n",
      "\n",
      "Epoch 4002/9999\n",
      "----------\n",
      "train Loss: 0.5452 Acc: 0.7090\n",
      "has spend time 142m 6s/n\n",
      "val Loss: 0.5431 Acc: 0.7320\n",
      "has spend time 142m 6s/n\n",
      "\n",
      "Epoch 4003/9999\n",
      "----------\n",
      "train Loss: 0.5163 Acc: 0.7418\n",
      "has spend time 142m 8s/n\n",
      "val Loss: 0.5591 Acc: 0.6993\n",
      "has spend time 142m 8s/n\n",
      "\n",
      "Epoch 4004/9999\n",
      "----------\n",
      "train Loss: 0.5154 Acc: 0.7254\n",
      "has spend time 142m 10s/n\n",
      "val Loss: 0.5418 Acc: 0.7190\n",
      "has spend time 142m 10s/n\n",
      "\n",
      "Epoch 4005/9999\n",
      "----------\n",
      "train Loss: 0.5014 Acc: 0.7418\n",
      "has spend time 142m 12s/n\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val Loss: 0.5536 Acc: 0.6928\n",
      "has spend time 142m 12s/n\n",
      "\n",
      "Epoch 4006/9999\n",
      "----------\n",
      "train Loss: 0.5149 Acc: 0.7582\n",
      "has spend time 142m 14s/n\n",
      "val Loss: 0.5583 Acc: 0.6993\n",
      "has spend time 142m 14s/n\n",
      "\n",
      "Epoch 4007/9999\n",
      "----------\n",
      "train Loss: 0.5010 Acc: 0.7418\n",
      "has spend time 142m 16s/n\n",
      "val Loss: 0.5487 Acc: 0.7124\n",
      "has spend time 142m 16s/n\n",
      "\n",
      "Epoch 4008/9999\n",
      "----------\n",
      "train Loss: 0.5056 Acc: 0.7377\n",
      "has spend time 142m 18s/n\n",
      "val Loss: 0.5517 Acc: 0.7124\n",
      "has spend time 142m 19s/n\n",
      "\n",
      "Epoch 4009/9999\n",
      "----------\n",
      "train Loss: 0.4949 Acc: 0.7418\n",
      "has spend time 142m 20s/n\n",
      "val Loss: 0.5500 Acc: 0.7124\n",
      "has spend time 142m 21s/n\n",
      "\n",
      "Epoch 4010/9999\n",
      "----------\n",
      "train Loss: 0.5245 Acc: 0.7049\n",
      "has spend time 142m 23s/n\n",
      "val Loss: 0.5437 Acc: 0.7190\n",
      "has spend time 142m 23s/n\n",
      "\n",
      "Epoch 4011/9999\n",
      "----------\n",
      "train Loss: 0.5180 Acc: 0.7418\n",
      "has spend time 142m 25s/n\n",
      "val Loss: 0.5464 Acc: 0.7059\n",
      "has spend time 142m 25s/n\n",
      "\n",
      "Epoch 4012/9999\n",
      "----------\n",
      "train Loss: 0.5329 Acc: 0.7172\n",
      "has spend time 142m 27s/n\n",
      "val Loss: 0.5525 Acc: 0.6993\n",
      "has spend time 142m 28s/n\n",
      "\n",
      "Epoch 4013/9999\n",
      "----------\n",
      "train Loss: 0.5040 Acc: 0.7787\n",
      "has spend time 142m 29s/n\n",
      "val Loss: 0.5504 Acc: 0.6993\n",
      "has spend time 142m 30s/n\n",
      "\n",
      "Epoch 4014/9999\n",
      "----------\n",
      "train Loss: 0.5366 Acc: 0.6967\n",
      "has spend time 142m 31s/n\n",
      "val Loss: 0.5514 Acc: 0.7124\n",
      "has spend time 142m 32s/n\n",
      "\n",
      "Epoch 4015/9999\n",
      "----------\n",
      "train Loss: 0.5444 Acc: 0.7295\n",
      "has spend time 142m 34s/n\n",
      "val Loss: 0.5484 Acc: 0.7255\n",
      "has spend time 142m 34s/n\n",
      "\n",
      "Epoch 4016/9999\n",
      "----------\n",
      "train Loss: 0.5262 Acc: 0.7090\n",
      "has spend time 142m 36s/n\n",
      "val Loss: 0.5543 Acc: 0.6928\n",
      "has spend time 142m 36s/n\n",
      "\n",
      "Epoch 4017/9999\n",
      "----------\n",
      "train Loss: 0.4940 Acc: 0.7623\n",
      "has spend time 142m 38s/n\n",
      "val Loss: 0.5533 Acc: 0.6928\n",
      "has spend time 142m 38s/n\n",
      "\n",
      "Epoch 4018/9999\n",
      "----------\n",
      "train Loss: 0.5106 Acc: 0.7172\n",
      "has spend time 142m 40s/n\n",
      "val Loss: 0.5536 Acc: 0.7059\n",
      "has spend time 142m 40s/n\n",
      "\n",
      "Epoch 4019/9999\n",
      "----------\n",
      "train Loss: 0.4997 Acc: 0.7582\n",
      "has spend time 142m 42s/n\n",
      "val Loss: 0.5573 Acc: 0.6993\n",
      "has spend time 142m 42s/n\n",
      "\n",
      "Epoch 4020/9999\n",
      "----------\n",
      "train Loss: 0.4919 Acc: 0.7541\n",
      "has spend time 142m 44s/n\n",
      "val Loss: 0.5498 Acc: 0.7124\n",
      "has spend time 142m 44s/n\n",
      "\n",
      "Epoch 4021/9999\n",
      "----------\n",
      "train Loss: 0.4898 Acc: 0.7418\n",
      "has spend time 142m 46s/n\n",
      "val Loss: 0.5602 Acc: 0.7059\n",
      "has spend time 142m 46s/n\n",
      "\n",
      "Epoch 4022/9999\n",
      "----------\n",
      "train Loss: 0.4865 Acc: 0.7664\n",
      "has spend time 142m 48s/n\n",
      "val Loss: 0.5593 Acc: 0.7059\n",
      "has spend time 142m 49s/n\n",
      "\n",
      "Epoch 4023/9999\n",
      "----------\n",
      "train Loss: 0.5233 Acc: 0.7172\n",
      "has spend time 142m 50s/n\n",
      "val Loss: 0.5412 Acc: 0.7190\n",
      "has spend time 142m 51s/n\n",
      "\n",
      "Epoch 4024/9999\n",
      "----------\n",
      "train Loss: 0.5300 Acc: 0.7377\n",
      "has spend time 142m 52s/n\n",
      "val Loss: 0.5484 Acc: 0.7124\n",
      "has spend time 142m 53s/n\n",
      "\n",
      "Epoch 4025/9999\n",
      "----------\n",
      "train Loss: 0.5387 Acc: 0.7254\n",
      "has spend time 142m 54s/n\n",
      "val Loss: 0.5421 Acc: 0.7124\n",
      "has spend time 142m 55s/n\n",
      "\n",
      "Epoch 4026/9999\n",
      "----------\n",
      "train Loss: 0.5134 Acc: 0.7582\n",
      "has spend time 142m 56s/n\n",
      "val Loss: 0.5497 Acc: 0.7059\n",
      "has spend time 142m 57s/n\n",
      "\n",
      "Epoch 4027/9999\n",
      "----------\n",
      "train Loss: 0.4748 Acc: 0.7541\n",
      "has spend time 142m 59s/n\n",
      "val Loss: 0.5497 Acc: 0.7124\n",
      "has spend time 142m 59s/n\n",
      "\n",
      "Epoch 4028/9999\n",
      "----------\n",
      "train Loss: 0.5477 Acc: 0.7049\n",
      "has spend time 143m 1s/n\n",
      "val Loss: 0.5524 Acc: 0.7124\n",
      "has spend time 143m 1s/n\n",
      "\n",
      "Epoch 4029/9999\n",
      "----------\n",
      "train Loss: 0.5324 Acc: 0.7377\n",
      "has spend time 143m 3s/n\n",
      "val Loss: 0.5593 Acc: 0.6993\n",
      "has spend time 143m 4s/n\n",
      "\n",
      "Epoch 4030/9999\n",
      "----------\n",
      "train Loss: 0.5105 Acc: 0.7418\n",
      "has spend time 143m 5s/n\n",
      "val Loss: 0.5535 Acc: 0.7124\n",
      "has spend time 143m 6s/n\n",
      "\n",
      "Epoch 4031/9999\n",
      "----------\n",
      "train Loss: 0.4721 Acc: 0.7746\n",
      "has spend time 143m 7s/n\n",
      "val Loss: 0.5445 Acc: 0.7124\n",
      "has spend time 143m 8s/n\n",
      "\n",
      "Epoch 4032/9999\n",
      "----------\n",
      "train Loss: 0.5139 Acc: 0.7418\n",
      "has spend time 143m 9s/n\n",
      "val Loss: 0.5639 Acc: 0.6928\n",
      "has spend time 143m 10s/n\n",
      "\n",
      "Epoch 4033/9999\n",
      "----------\n",
      "train Loss: 0.5268 Acc: 0.7418\n",
      "has spend time 143m 12s/n\n",
      "val Loss: 0.5519 Acc: 0.6993\n",
      "has spend time 143m 12s/n\n",
      "\n",
      "Epoch 4034/9999\n",
      "----------\n",
      "train Loss: 0.5329 Acc: 0.7377\n",
      "has spend time 143m 14s/n\n",
      "val Loss: 0.5445 Acc: 0.7190\n",
      "has spend time 143m 15s/n\n",
      "\n",
      "Epoch 4035/9999\n",
      "----------\n",
      "train Loss: 0.5260 Acc: 0.7459\n",
      "has spend time 143m 16s/n\n",
      "val Loss: 0.5507 Acc: 0.7059\n",
      "has spend time 143m 17s/n\n",
      "\n",
      "Epoch 4036/9999\n",
      "----------\n",
      "train Loss: 0.5273 Acc: 0.6967\n",
      "has spend time 143m 18s/n\n",
      "val Loss: 0.5488 Acc: 0.7124\n",
      "has spend time 143m 19s/n\n",
      "\n",
      "Epoch 4037/9999\n",
      "----------\n",
      "train Loss: 0.5070 Acc: 0.7418\n",
      "has spend time 143m 21s/n\n",
      "val Loss: 0.5513 Acc: 0.7255\n",
      "has spend time 143m 21s/n\n",
      "\n",
      "Epoch 4038/9999\n",
      "----------\n",
      "train Loss: 0.4912 Acc: 0.7582\n",
      "has spend time 143m 23s/n\n",
      "val Loss: 0.5542 Acc: 0.7059\n",
      "has spend time 143m 23s/n\n",
      "\n",
      "Epoch 4039/9999\n",
      "----------\n",
      "train Loss: 0.5123 Acc: 0.7008\n",
      "has spend time 143m 25s/n\n",
      "val Loss: 0.5528 Acc: 0.7059\n",
      "has spend time 143m 25s/n\n",
      "\n",
      "Epoch 4040/9999\n",
      "----------\n",
      "train Loss: 0.4722 Acc: 0.7787\n",
      "has spend time 143m 27s/n\n",
      "val Loss: 0.5453 Acc: 0.7059\n",
      "has spend time 143m 27s/n\n",
      "\n",
      "Epoch 4041/9999\n",
      "----------\n",
      "train Loss: 0.5098 Acc: 0.7459\n",
      "has spend time 143m 29s/n\n",
      "val Loss: 0.5489 Acc: 0.7059\n",
      "has spend time 143m 30s/n\n",
      "\n",
      "Epoch 4042/9999\n",
      "----------\n",
      "train Loss: 0.4867 Acc: 0.7336\n",
      "has spend time 143m 32s/n\n",
      "val Loss: 0.5457 Acc: 0.7124\n",
      "has spend time 143m 32s/n\n",
      "\n",
      "Epoch 4043/9999\n",
      "----------\n",
      "train Loss: 0.5081 Acc: 0.7254\n",
      "has spend time 143m 34s/n\n",
      "val Loss: 0.5517 Acc: 0.7124\n",
      "has spend time 143m 34s/n\n",
      "\n",
      "Epoch 4044/9999\n",
      "----------\n",
      "train Loss: 0.5130 Acc: 0.7254\n",
      "has spend time 143m 36s/n\n",
      "val Loss: 0.5529 Acc: 0.7059\n",
      "has spend time 143m 37s/n\n",
      "\n",
      "Epoch 4045/9999\n",
      "----------\n",
      "train Loss: 0.4943 Acc: 0.7500\n",
      "has spend time 143m 38s/n\n",
      "val Loss: 0.5469 Acc: 0.7124\n",
      "has spend time 143m 39s/n\n",
      "\n",
      "Epoch 4046/9999\n",
      "----------\n",
      "train Loss: 0.5036 Acc: 0.7377\n",
      "has spend time 143m 40s/n\n",
      "val Loss: 0.5399 Acc: 0.7255\n",
      "has spend time 143m 41s/n\n",
      "\n",
      "Epoch 4047/9999\n",
      "----------\n",
      "train Loss: 0.5107 Acc: 0.7500\n",
      "has spend time 143m 43s/n\n",
      "val Loss: 0.5478 Acc: 0.7059\n",
      "has spend time 143m 43s/n\n",
      "\n",
      "Epoch 4048/9999\n",
      "----------\n",
      "train Loss: 0.5308 Acc: 0.6926\n",
      "has spend time 143m 45s/n\n",
      "val Loss: 0.5525 Acc: 0.7124\n",
      "has spend time 143m 46s/n\n",
      "\n",
      "Epoch 4049/9999\n",
      "----------\n",
      "train Loss: 0.5284 Acc: 0.7295\n",
      "has spend time 143m 47s/n\n",
      "val Loss: 0.5498 Acc: 0.6928\n",
      "has spend time 143m 48s/n\n",
      "\n",
      "Epoch 4050/9999\n",
      "----------\n",
      "train Loss: 0.4948 Acc: 0.7459\n",
      "has spend time 143m 49s/n\n",
      "val Loss: 0.5453 Acc: 0.7124\n",
      "has spend time 143m 50s/n\n",
      "\n",
      "Epoch 4051/9999\n",
      "----------\n",
      "train Loss: 0.5595 Acc: 0.6926\n",
      "has spend time 143m 51s/n\n",
      "val Loss: 0.5442 Acc: 0.7124\n",
      "has spend time 143m 52s/n\n",
      "\n",
      "Epoch 4052/9999\n",
      "----------\n",
      "train Loss: 0.4853 Acc: 0.7377\n",
      "has spend time 143m 53s/n\n",
      "val Loss: 0.5446 Acc: 0.7190\n",
      "has spend time 143m 54s/n\n",
      "\n",
      "Epoch 4053/9999\n",
      "----------\n",
      "train Loss: 0.5247 Acc: 0.7295\n",
      "has spend time 143m 55s/n\n",
      "val Loss: 0.5396 Acc: 0.7190\n",
      "has spend time 143m 56s/n\n",
      "\n",
      "Epoch 4054/9999\n",
      "----------\n",
      "train Loss: 0.5097 Acc: 0.7254\n",
      "has spend time 143m 58s/n\n",
      "val Loss: 0.5444 Acc: 0.7059\n",
      "has spend time 143m 58s/n\n",
      "\n",
      "Epoch 4055/9999\n",
      "----------\n",
      "train Loss: 0.4957 Acc: 0.7664\n",
      "has spend time 143m 60s/n\n",
      "val Loss: 0.5473 Acc: 0.7059\n",
      "has spend time 144m 0s/n\n",
      "\n",
      "Epoch 4056/9999\n",
      "----------\n",
      "train Loss: 0.5050 Acc: 0.7254\n",
      "has spend time 144m 2s/n\n",
      "val Loss: 0.5553 Acc: 0.6993\n",
      "has spend time 144m 2s/n\n",
      "\n",
      "Epoch 4057/9999\n",
      "----------\n",
      "train Loss: 0.5110 Acc: 0.7295\n",
      "has spend time 144m 4s/n\n",
      "val Loss: 0.5462 Acc: 0.7059\n",
      "has spend time 144m 4s/n\n",
      "\n",
      "Epoch 4058/9999\n",
      "----------\n",
      "train Loss: 0.5128 Acc: 0.7008\n",
      "has spend time 144m 6s/n\n",
      "val Loss: 0.5464 Acc: 0.7124\n",
      "has spend time 144m 7s/n\n",
      "\n",
      "Epoch 4059/9999\n",
      "----------\n",
      "train Loss: 0.5415 Acc: 0.7418\n",
      "has spend time 144m 8s/n\n",
      "val Loss: 0.5462 Acc: 0.7059\n",
      "has spend time 144m 9s/n\n",
      "\n",
      "Epoch 4060/9999\n",
      "----------\n",
      "train Loss: 0.5203 Acc: 0.7213\n",
      "has spend time 144m 10s/n\n",
      "val Loss: 0.5479 Acc: 0.7124\n",
      "has spend time 144m 11s/n\n",
      "\n",
      "Epoch 4061/9999\n",
      "----------\n",
      "train Loss: 0.5165 Acc: 0.7582\n",
      "has spend time 144m 12s/n\n",
      "val Loss: 0.5481 Acc: 0.7124\n",
      "has spend time 144m 13s/n\n",
      "\n",
      "Epoch 4062/9999\n",
      "----------\n",
      "train Loss: 0.5135 Acc: 0.7295\n",
      "has spend time 144m 14s/n\n",
      "val Loss: 0.5560 Acc: 0.7059\n",
      "has spend time 144m 15s/n\n",
      "\n",
      "Epoch 4063/9999\n",
      "----------\n",
      "train Loss: 0.4811 Acc: 0.7336\n",
      "has spend time 144m 17s/n\n",
      "val Loss: 0.5686 Acc: 0.6993\n",
      "has spend time 144m 17s/n\n",
      "\n",
      "Epoch 4064/9999\n",
      "----------\n",
      "train Loss: 0.5175 Acc: 0.7295\n",
      "has spend time 144m 19s/n\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val Loss: 0.5623 Acc: 0.6993\n",
      "has spend time 144m 19s/n\n",
      "\n",
      "Epoch 4065/9999\n",
      "----------\n",
      "train Loss: 0.5167 Acc: 0.7254\n",
      "has spend time 144m 21s/n\n",
      "val Loss: 0.5498 Acc: 0.7059\n",
      "has spend time 144m 22s/n\n",
      "\n",
      "Epoch 4066/9999\n",
      "----------\n",
      "train Loss: 0.5315 Acc: 0.7131\n",
      "has spend time 144m 23s/n\n",
      "val Loss: 0.5486 Acc: 0.7255\n",
      "has spend time 144m 24s/n\n",
      "\n",
      "Epoch 4067/9999\n",
      "----------\n",
      "train Loss: 0.4814 Acc: 0.7377\n",
      "has spend time 144m 25s/n\n",
      "val Loss: 0.5505 Acc: 0.7124\n",
      "has spend time 144m 26s/n\n",
      "\n",
      "Epoch 4068/9999\n",
      "----------\n",
      "train Loss: 0.5126 Acc: 0.7500\n",
      "has spend time 144m 27s/n\n",
      "val Loss: 0.5658 Acc: 0.6928\n",
      "has spend time 144m 28s/n\n",
      "\n",
      "Epoch 4069/9999\n",
      "----------\n",
      "train Loss: 0.4922 Acc: 0.7336\n",
      "has spend time 144m 29s/n\n",
      "val Loss: 0.5442 Acc: 0.7190\n",
      "has spend time 144m 30s/n\n",
      "\n",
      "Epoch 4070/9999\n",
      "----------\n",
      "train Loss: 0.5125 Acc: 0.7090\n",
      "has spend time 144m 31s/n\n",
      "val Loss: 0.5506 Acc: 0.7059\n",
      "has spend time 144m 32s/n\n",
      "\n",
      "Epoch 4071/9999\n",
      "----------\n",
      "train Loss: 0.5145 Acc: 0.7418\n",
      "has spend time 144m 34s/n\n",
      "val Loss: 0.5619 Acc: 0.6993\n",
      "has spend time 144m 34s/n\n",
      "\n",
      "Epoch 4072/9999\n",
      "----------\n",
      "train Loss: 0.5076 Acc: 0.7746\n",
      "has spend time 144m 36s/n\n",
      "val Loss: 0.5605 Acc: 0.7059\n",
      "has spend time 144m 36s/n\n",
      "\n",
      "Epoch 4073/9999\n",
      "----------\n",
      "train Loss: 0.5062 Acc: 0.7541\n",
      "has spend time 144m 38s/n\n",
      "val Loss: 0.5581 Acc: 0.7059\n",
      "has spend time 144m 38s/n\n",
      "\n",
      "Epoch 4074/9999\n",
      "----------\n",
      "train Loss: 0.4903 Acc: 0.7623\n",
      "has spend time 144m 40s/n\n",
      "val Loss: 0.5522 Acc: 0.7124\n",
      "has spend time 144m 40s/n\n",
      "\n",
      "Epoch 4075/9999\n",
      "----------\n",
      "train Loss: 0.4798 Acc: 0.7582\n",
      "has spend time 144m 42s/n\n",
      "val Loss: 0.5558 Acc: 0.6993\n",
      "has spend time 144m 42s/n\n",
      "\n",
      "Epoch 4076/9999\n",
      "----------\n",
      "train Loss: 0.4805 Acc: 0.7787\n",
      "has spend time 144m 44s/n\n",
      "val Loss: 0.5475 Acc: 0.7124\n",
      "has spend time 144m 45s/n\n",
      "\n",
      "Epoch 4077/9999\n",
      "----------\n",
      "train Loss: 0.5288 Acc: 0.7049\n",
      "has spend time 144m 46s/n\n",
      "val Loss: 0.5547 Acc: 0.7059\n",
      "has spend time 144m 47s/n\n",
      "\n",
      "Epoch 4078/9999\n",
      "----------\n",
      "train Loss: 0.4955 Acc: 0.7172\n",
      "has spend time 144m 48s/n\n",
      "val Loss: 0.5463 Acc: 0.7124\n",
      "has spend time 144m 49s/n\n",
      "\n",
      "Epoch 4079/9999\n",
      "----------\n",
      "train Loss: 0.5369 Acc: 0.7049\n",
      "has spend time 144m 50s/n\n",
      "val Loss: 0.5508 Acc: 0.7190\n",
      "has spend time 144m 51s/n\n",
      "\n",
      "Epoch 4080/9999\n",
      "----------\n",
      "train Loss: 0.5022 Acc: 0.7377\n",
      "has spend time 144m 52s/n\n",
      "val Loss: 0.5435 Acc: 0.7190\n",
      "has spend time 144m 53s/n\n",
      "\n",
      "Epoch 4081/9999\n",
      "----------\n",
      "train Loss: 0.5204 Acc: 0.7336\n",
      "has spend time 144m 55s/n\n",
      "val Loss: 0.5619 Acc: 0.7059\n",
      "has spend time 144m 55s/n\n",
      "\n",
      "Epoch 4082/9999\n",
      "----------\n",
      "train Loss: 0.5049 Acc: 0.7705\n",
      "has spend time 144m 57s/n\n",
      "val Loss: 0.5710 Acc: 0.6732\n",
      "has spend time 144m 57s/n\n",
      "\n",
      "Epoch 4083/9999\n",
      "----------\n",
      "train Loss: 0.5303 Acc: 0.7172\n",
      "has spend time 144m 59s/n\n",
      "val Loss: 0.5590 Acc: 0.6863\n",
      "has spend time 144m 59s/n\n",
      "\n",
      "Epoch 4084/9999\n",
      "----------\n",
      "train Loss: 0.5340 Acc: 0.7336\n",
      "has spend time 145m 1s/n\n",
      "val Loss: 0.5451 Acc: 0.7255\n",
      "has spend time 145m 1s/n\n",
      "\n",
      "Epoch 4085/9999\n",
      "----------\n",
      "train Loss: 0.4916 Acc: 0.7705\n",
      "has spend time 145m 3s/n\n",
      "val Loss: 0.5480 Acc: 0.7124\n",
      "has spend time 145m 4s/n\n",
      "\n",
      "Epoch 4086/9999\n",
      "----------\n",
      "train Loss: 0.5007 Acc: 0.7336\n",
      "has spend time 145m 5s/n\n",
      "val Loss: 0.5618 Acc: 0.6863\n",
      "has spend time 145m 6s/n\n",
      "\n",
      "Epoch 4087/9999\n",
      "----------\n",
      "train Loss: 0.5058 Acc: 0.7623\n",
      "has spend time 145m 8s/n\n",
      "val Loss: 0.5575 Acc: 0.7059\n",
      "has spend time 145m 8s/n\n",
      "\n",
      "Epoch 4088/9999\n",
      "----------\n",
      "train Loss: 0.5123 Acc: 0.7172\n",
      "has spend time 145m 10s/n\n",
      "val Loss: 0.5593 Acc: 0.6928\n",
      "has spend time 145m 10s/n\n",
      "\n",
      "Epoch 4089/9999\n",
      "----------\n",
      "train Loss: 0.5118 Acc: 0.7500\n",
      "has spend time 145m 12s/n\n",
      "val Loss: 0.5458 Acc: 0.7190\n",
      "has spend time 145m 12s/n\n",
      "\n",
      "Epoch 4090/9999\n",
      "----------\n",
      "train Loss: 0.5080 Acc: 0.7418\n",
      "has spend time 145m 14s/n\n",
      "val Loss: 0.5529 Acc: 0.7124\n",
      "has spend time 145m 15s/n\n",
      "\n",
      "Epoch 4091/9999\n",
      "----------\n",
      "train Loss: 0.5220 Acc: 0.7172\n",
      "has spend time 145m 16s/n\n",
      "val Loss: 0.5508 Acc: 0.6993\n",
      "has spend time 145m 17s/n\n",
      "\n",
      "Epoch 4092/9999\n",
      "----------\n",
      "train Loss: 0.5053 Acc: 0.7008\n",
      "has spend time 145m 18s/n\n",
      "val Loss: 0.5534 Acc: 0.7190\n",
      "has spend time 145m 19s/n\n",
      "\n",
      "Epoch 4093/9999\n",
      "----------\n",
      "train Loss: 0.5194 Acc: 0.7336\n",
      "has spend time 145m 20s/n\n",
      "val Loss: 0.5445 Acc: 0.7124\n",
      "has spend time 145m 21s/n\n",
      "\n",
      "Epoch 4094/9999\n",
      "----------\n",
      "train Loss: 0.4951 Acc: 0.7459\n",
      "has spend time 145m 22s/n\n",
      "val Loss: 0.5389 Acc: 0.7059\n",
      "has spend time 145m 23s/n\n",
      "\n",
      "Epoch 4095/9999\n",
      "----------\n",
      "train Loss: 0.4762 Acc: 0.7746\n",
      "has spend time 145m 24s/n\n",
      "val Loss: 0.5523 Acc: 0.6993\n",
      "has spend time 145m 25s/n\n",
      "\n",
      "Epoch 4096/9999\n",
      "----------\n",
      "train Loss: 0.5074 Acc: 0.7582\n",
      "has spend time 145m 26s/n\n",
      "val Loss: 0.5525 Acc: 0.7059\n",
      "has spend time 145m 27s/n\n",
      "\n",
      "Epoch 4097/9999\n",
      "----------\n",
      "train Loss: 0.4720 Acc: 0.7746\n",
      "has spend time 145m 29s/n\n",
      "val Loss: 0.5584 Acc: 0.7059\n",
      "has spend time 145m 29s/n\n",
      "\n",
      "Epoch 4098/9999\n",
      "----------\n",
      "train Loss: 0.5406 Acc: 0.7172\n",
      "has spend time 145m 31s/n\n",
      "val Loss: 0.5457 Acc: 0.7059\n",
      "has spend time 145m 32s/n\n",
      "\n",
      "Epoch 4099/9999\n",
      "----------\n",
      "train Loss: 0.5086 Acc: 0.7295\n",
      "has spend time 145m 33s/n\n",
      "val Loss: 0.5438 Acc: 0.7190\n",
      "has spend time 145m 34s/n\n",
      "\n",
      "Epoch 4100/9999\n",
      "----------\n",
      "train Loss: 0.5246 Acc: 0.7049\n",
      "has spend time 145m 35s/n\n",
      "val Loss: 0.5621 Acc: 0.7059\n",
      "has spend time 145m 36s/n\n",
      "\n",
      "Epoch 4101/9999\n",
      "----------\n",
      "train Loss: 0.4781 Acc: 0.7705\n",
      "has spend time 145m 38s/n\n",
      "val Loss: 0.5476 Acc: 0.7059\n",
      "has spend time 145m 38s/n\n",
      "\n",
      "Epoch 4102/9999\n",
      "----------\n",
      "train Loss: 0.5116 Acc: 0.7172\n",
      "has spend time 145m 40s/n\n",
      "val Loss: 0.5543 Acc: 0.7059\n",
      "has spend time 145m 40s/n\n",
      "\n",
      "Epoch 4103/9999\n",
      "----------\n",
      "train Loss: 0.5223 Acc: 0.7500\n",
      "has spend time 145m 42s/n\n",
      "val Loss: 0.5444 Acc: 0.7190\n",
      "has spend time 145m 42s/n\n",
      "\n",
      "Epoch 4104/9999\n",
      "----------\n",
      "train Loss: 0.4578 Acc: 0.7705\n",
      "has spend time 145m 44s/n\n",
      "val Loss: 0.5670 Acc: 0.7059\n",
      "has spend time 145m 44s/n\n",
      "\n",
      "Epoch 4105/9999\n",
      "----------\n",
      "train Loss: 0.5301 Acc: 0.7295\n",
      "has spend time 145m 46s/n\n",
      "val Loss: 0.5463 Acc: 0.7124\n",
      "has spend time 145m 46s/n\n",
      "\n",
      "Epoch 4106/9999\n",
      "----------\n",
      "train Loss: 0.5010 Acc: 0.7582\n",
      "has spend time 145m 48s/n\n",
      "val Loss: 0.5584 Acc: 0.6863\n",
      "has spend time 145m 48s/n\n",
      "\n",
      "Epoch 4107/9999\n",
      "----------\n",
      "train Loss: 0.5178 Acc: 0.7459\n",
      "has spend time 145m 50s/n\n",
      "val Loss: 0.5432 Acc: 0.7124\n",
      "has spend time 145m 50s/n\n",
      "\n",
      "Epoch 4108/9999\n",
      "----------\n",
      "train Loss: 0.4977 Acc: 0.7377\n",
      "has spend time 145m 52s/n\n",
      "val Loss: 0.5449 Acc: 0.7059\n",
      "has spend time 145m 52s/n\n",
      "\n",
      "Epoch 4109/9999\n",
      "----------\n",
      "train Loss: 0.4915 Acc: 0.7541\n",
      "has spend time 145m 54s/n\n",
      "val Loss: 0.5431 Acc: 0.7190\n",
      "has spend time 145m 55s/n\n",
      "\n",
      "Epoch 4110/9999\n",
      "----------\n",
      "train Loss: 0.4988 Acc: 0.7541\n",
      "has spend time 145m 56s/n\n",
      "val Loss: 0.5523 Acc: 0.7190\n",
      "has spend time 145m 57s/n\n",
      "\n",
      "Epoch 4111/9999\n",
      "----------\n",
      "train Loss: 0.5313 Acc: 0.7254\n",
      "has spend time 145m 58s/n\n",
      "val Loss: 0.5390 Acc: 0.7124\n",
      "has spend time 145m 59s/n\n",
      "\n",
      "Epoch 4112/9999\n",
      "----------\n",
      "train Loss: 0.5057 Acc: 0.7664\n",
      "has spend time 146m 0s/n\n",
      "val Loss: 0.5458 Acc: 0.7124\n",
      "has spend time 146m 1s/n\n",
      "\n",
      "Epoch 4113/9999\n",
      "----------\n",
      "train Loss: 0.5244 Acc: 0.7008\n",
      "has spend time 146m 2s/n\n",
      "val Loss: 0.5417 Acc: 0.7255\n",
      "has spend time 146m 3s/n\n",
      "\n",
      "Epoch 4114/9999\n",
      "----------\n",
      "train Loss: 0.5143 Acc: 0.7254\n",
      "has spend time 146m 4s/n\n",
      "val Loss: 0.5474 Acc: 0.7255\n",
      "has spend time 146m 5s/n\n",
      "\n",
      "Epoch 4115/9999\n",
      "----------\n",
      "train Loss: 0.4776 Acc: 0.7541\n",
      "has spend time 146m 6s/n\n",
      "val Loss: 0.5452 Acc: 0.7124\n",
      "has spend time 146m 7s/n\n",
      "\n",
      "Epoch 4116/9999\n",
      "----------\n",
      "train Loss: 0.4863 Acc: 0.7664\n",
      "has spend time 146m 8s/n\n",
      "val Loss: 0.5544 Acc: 0.6993\n",
      "has spend time 146m 9s/n\n",
      "\n",
      "Epoch 4117/9999\n",
      "----------\n",
      "train Loss: 0.4757 Acc: 0.7623\n",
      "has spend time 146m 10s/n\n",
      "val Loss: 0.5449 Acc: 0.7124\n",
      "has spend time 146m 11s/n\n",
      "\n",
      "Epoch 4118/9999\n",
      "----------\n",
      "train Loss: 0.4920 Acc: 0.7254\n",
      "has spend time 146m 12s/n\n",
      "val Loss: 0.5415 Acc: 0.7124\n",
      "has spend time 146m 13s/n\n",
      "\n",
      "Epoch 4119/9999\n",
      "----------\n",
      "train Loss: 0.5474 Acc: 0.7090\n",
      "has spend time 146m 14s/n\n",
      "val Loss: 0.5491 Acc: 0.6993\n",
      "has spend time 146m 15s/n\n",
      "\n",
      "Epoch 4120/9999\n",
      "----------\n",
      "train Loss: 0.5470 Acc: 0.7131\n",
      "has spend time 146m 16s/n\n",
      "val Loss: 0.5702 Acc: 0.6993\n",
      "has spend time 146m 17s/n\n",
      "\n",
      "Epoch 4121/9999\n",
      "----------\n",
      "train Loss: 0.5248 Acc: 0.7254\n",
      "has spend time 146m 18s/n\n",
      "val Loss: 0.5522 Acc: 0.7124\n",
      "has spend time 146m 19s/n\n",
      "\n",
      "Epoch 4122/9999\n",
      "----------\n",
      "train Loss: 0.5027 Acc: 0.7418\n",
      "has spend time 146m 20s/n\n",
      "val Loss: 0.5495 Acc: 0.7124\n",
      "has spend time 146m 21s/n\n",
      "\n",
      "Epoch 4123/9999\n",
      "----------\n",
      "train Loss: 0.5141 Acc: 0.7295\n",
      "has spend time 146m 23s/n\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val Loss: 0.5599 Acc: 0.6928\n",
      "has spend time 146m 23s/n\n",
      "\n",
      "Epoch 4124/9999\n",
      "----------\n",
      "train Loss: 0.5296 Acc: 0.7131\n",
      "has spend time 146m 25s/n\n",
      "val Loss: 0.5645 Acc: 0.6863\n",
      "has spend time 146m 25s/n\n",
      "\n",
      "Epoch 4125/9999\n",
      "----------\n",
      "train Loss: 0.5071 Acc: 0.7623\n",
      "has spend time 146m 27s/n\n",
      "val Loss: 0.5533 Acc: 0.7059\n",
      "has spend time 146m 27s/n\n",
      "\n",
      "Epoch 4126/9999\n",
      "----------\n",
      "train Loss: 0.4895 Acc: 0.7418\n",
      "has spend time 146m 29s/n\n",
      "val Loss: 0.5734 Acc: 0.6928\n",
      "has spend time 146m 29s/n\n",
      "\n",
      "Epoch 4127/9999\n",
      "----------\n",
      "train Loss: 0.5217 Acc: 0.7459\n",
      "has spend time 146m 31s/n\n",
      "val Loss: 0.5465 Acc: 0.7124\n",
      "has spend time 146m 31s/n\n",
      "\n",
      "Epoch 4128/9999\n",
      "----------\n",
      "train Loss: 0.5244 Acc: 0.7049\n",
      "has spend time 146m 33s/n\n",
      "val Loss: 0.5517 Acc: 0.6928\n",
      "has spend time 146m 34s/n\n",
      "\n",
      "Epoch 4129/9999\n",
      "----------\n",
      "train Loss: 0.4821 Acc: 0.7500\n",
      "has spend time 146m 35s/n\n",
      "val Loss: 0.5485 Acc: 0.7124\n",
      "has spend time 146m 36s/n\n",
      "\n",
      "Epoch 4130/9999\n",
      "----------\n",
      "train Loss: 0.4887 Acc: 0.7295\n",
      "has spend time 146m 38s/n\n",
      "val Loss: 0.5639 Acc: 0.6993\n",
      "has spend time 146m 38s/n\n",
      "\n",
      "Epoch 4131/9999\n",
      "----------\n",
      "train Loss: 0.5099 Acc: 0.7213\n",
      "has spend time 146m 40s/n\n",
      "val Loss: 0.5491 Acc: 0.6993\n",
      "has spend time 146m 40s/n\n",
      "\n",
      "Epoch 4132/9999\n",
      "----------\n",
      "train Loss: 0.5072 Acc: 0.7541\n",
      "has spend time 146m 42s/n\n",
      "val Loss: 0.5597 Acc: 0.6993\n",
      "has spend time 146m 43s/n\n",
      "\n",
      "Epoch 4133/9999\n",
      "----------\n",
      "train Loss: 0.5312 Acc: 0.7459\n",
      "has spend time 146m 44s/n\n",
      "val Loss: 0.5446 Acc: 0.7059\n",
      "has spend time 146m 45s/n\n",
      "\n",
      "Epoch 4134/9999\n",
      "----------\n",
      "train Loss: 0.5222 Acc: 0.7418\n",
      "has spend time 146m 46s/n\n",
      "val Loss: 0.5557 Acc: 0.6928\n",
      "has spend time 146m 47s/n\n",
      "\n",
      "Epoch 4135/9999\n",
      "----------\n",
      "train Loss: 0.5169 Acc: 0.7213\n",
      "has spend time 146m 48s/n\n",
      "val Loss: 0.5442 Acc: 0.7124\n",
      "has spend time 146m 49s/n\n",
      "\n",
      "Epoch 4136/9999\n",
      "----------\n",
      "train Loss: 0.5089 Acc: 0.7213\n",
      "has spend time 146m 50s/n\n",
      "val Loss: 0.5557 Acc: 0.7059\n",
      "has spend time 146m 51s/n\n",
      "\n",
      "Epoch 4137/9999\n",
      "----------\n",
      "train Loss: 0.5210 Acc: 0.7254\n",
      "has spend time 146m 52s/n\n",
      "val Loss: 0.5501 Acc: 0.6993\n",
      "has spend time 146m 53s/n\n",
      "\n",
      "Epoch 4138/9999\n",
      "----------\n",
      "train Loss: 0.4993 Acc: 0.7500\n",
      "has spend time 146m 54s/n\n",
      "val Loss: 0.5462 Acc: 0.7190\n",
      "has spend time 146m 55s/n\n",
      "\n",
      "Epoch 4139/9999\n",
      "----------\n",
      "train Loss: 0.4838 Acc: 0.7664\n",
      "has spend time 146m 56s/n\n",
      "val Loss: 0.5418 Acc: 0.7124\n",
      "has spend time 146m 57s/n\n",
      "\n",
      "Epoch 4140/9999\n",
      "----------\n",
      "train Loss: 0.5016 Acc: 0.7500\n",
      "has spend time 146m 59s/n\n",
      "val Loss: 0.5424 Acc: 0.7190\n",
      "has spend time 146m 59s/n\n",
      "\n",
      "Epoch 4141/9999\n",
      "----------\n",
      "train Loss: 0.5066 Acc: 0.7295\n",
      "has spend time 147m 1s/n\n",
      "val Loss: 0.5426 Acc: 0.7190\n",
      "has spend time 147m 1s/n\n",
      "\n",
      "Epoch 4142/9999\n",
      "----------\n",
      "train Loss: 0.5244 Acc: 0.7418\n",
      "has spend time 147m 3s/n\n",
      "val Loss: 0.5571 Acc: 0.6993\n",
      "has spend time 147m 4s/n\n",
      "\n",
      "Epoch 4143/9999\n",
      "----------\n",
      "train Loss: 0.5120 Acc: 0.7582\n",
      "has spend time 147m 5s/n\n",
      "val Loss: 0.5507 Acc: 0.7059\n",
      "has spend time 147m 6s/n\n",
      "\n",
      "Epoch 4144/9999\n",
      "----------\n",
      "train Loss: 0.5335 Acc: 0.7336\n",
      "has spend time 147m 7s/n\n",
      "val Loss: 0.5505 Acc: 0.7059\n",
      "has spend time 147m 8s/n\n",
      "\n",
      "Epoch 4145/9999\n",
      "----------\n",
      "train Loss: 0.4873 Acc: 0.7500\n",
      "has spend time 147m 9s/n\n",
      "val Loss: 0.5531 Acc: 0.7059\n",
      "has spend time 147m 10s/n\n",
      "\n",
      "Epoch 4146/9999\n",
      "----------\n",
      "train Loss: 0.4930 Acc: 0.7664\n",
      "has spend time 147m 11s/n\n",
      "val Loss: 0.5468 Acc: 0.7124\n",
      "has spend time 147m 12s/n\n",
      "\n",
      "Epoch 4147/9999\n",
      "----------\n",
      "train Loss: 0.5112 Acc: 0.7049\n",
      "has spend time 147m 13s/n\n",
      "val Loss: 0.5501 Acc: 0.6993\n",
      "has spend time 147m 14s/n\n",
      "\n",
      "Epoch 4148/9999\n",
      "----------\n",
      "train Loss: 0.4791 Acc: 0.7705\n",
      "has spend time 147m 16s/n\n",
      "val Loss: 0.5571 Acc: 0.6928\n",
      "has spend time 147m 16s/n\n",
      "\n",
      "Epoch 4149/9999\n",
      "----------\n",
      "train Loss: 0.5114 Acc: 0.7090\n",
      "has spend time 147m 18s/n\n",
      "val Loss: 0.5512 Acc: 0.7059\n",
      "has spend time 147m 19s/n\n",
      "\n",
      "Epoch 4150/9999\n",
      "----------\n",
      "train Loss: 0.5258 Acc: 0.7377\n",
      "has spend time 147m 20s/n\n",
      "val Loss: 0.5563 Acc: 0.7124\n",
      "has spend time 147m 21s/n\n",
      "\n",
      "Epoch 4151/9999\n",
      "----------\n",
      "train Loss: 0.5065 Acc: 0.7254\n",
      "has spend time 147m 22s/n\n",
      "val Loss: 0.5558 Acc: 0.7059\n",
      "has spend time 147m 23s/n\n",
      "\n",
      "Epoch 4152/9999\n",
      "----------\n",
      "train Loss: 0.5242 Acc: 0.7377\n",
      "has spend time 147m 24s/n\n",
      "val Loss: 0.5465 Acc: 0.7059\n",
      "has spend time 147m 25s/n\n",
      "\n",
      "Epoch 4153/9999\n",
      "----------\n",
      "train Loss: 0.5440 Acc: 0.7049\n",
      "has spend time 147m 26s/n\n",
      "val Loss: 0.5452 Acc: 0.7124\n",
      "has spend time 147m 27s/n\n",
      "\n",
      "Epoch 4154/9999\n",
      "----------\n",
      "train Loss: 0.4912 Acc: 0.7664\n",
      "has spend time 147m 28s/n\n",
      "val Loss: 0.5484 Acc: 0.7059\n",
      "has spend time 147m 29s/n\n",
      "\n",
      "Epoch 4155/9999\n",
      "----------\n",
      "train Loss: 0.5116 Acc: 0.7254\n",
      "has spend time 147m 31s/n\n",
      "val Loss: 0.5506 Acc: 0.7124\n",
      "has spend time 147m 31s/n\n",
      "\n",
      "Epoch 4156/9999\n",
      "----------\n",
      "train Loss: 0.5250 Acc: 0.7049\n",
      "has spend time 147m 33s/n\n",
      "val Loss: 0.5525 Acc: 0.6993\n",
      "has spend time 147m 34s/n\n",
      "\n",
      "Epoch 4157/9999\n",
      "----------\n",
      "train Loss: 0.5215 Acc: 0.7336\n",
      "has spend time 147m 35s/n\n",
      "val Loss: 0.5517 Acc: 0.7059\n",
      "has spend time 147m 36s/n\n",
      "\n",
      "Epoch 4158/9999\n",
      "----------\n",
      "train Loss: 0.5369 Acc: 0.7295\n",
      "has spend time 147m 38s/n\n",
      "val Loss: 0.5663 Acc: 0.6928\n",
      "has spend time 147m 38s/n\n",
      "\n",
      "Epoch 4159/9999\n",
      "----------\n",
      "train Loss: 0.4912 Acc: 0.7623\n",
      "has spend time 147m 40s/n\n",
      "val Loss: 0.5620 Acc: 0.6993\n",
      "has spend time 147m 40s/n\n",
      "\n",
      "Epoch 4160/9999\n",
      "----------\n",
      "train Loss: 0.5140 Acc: 0.7377\n",
      "has spend time 147m 42s/n\n",
      "val Loss: 0.5548 Acc: 0.6928\n",
      "has spend time 147m 43s/n\n",
      "\n",
      "Epoch 4161/9999\n",
      "----------\n",
      "train Loss: 0.5101 Acc: 0.7090\n",
      "has spend time 147m 44s/n\n",
      "val Loss: 0.5563 Acc: 0.7059\n",
      "has spend time 147m 45s/n\n",
      "\n",
      "Epoch 4162/9999\n",
      "----------\n",
      "train Loss: 0.5422 Acc: 0.6967\n",
      "has spend time 147m 46s/n\n",
      "val Loss: 0.5402 Acc: 0.7190\n",
      "has spend time 147m 47s/n\n",
      "\n",
      "Epoch 4163/9999\n",
      "----------\n",
      "train Loss: 0.5227 Acc: 0.7172\n",
      "has spend time 147m 49s/n\n",
      "val Loss: 0.5402 Acc: 0.7190\n",
      "has spend time 147m 49s/n\n",
      "\n",
      "Epoch 4164/9999\n",
      "----------\n",
      "train Loss: 0.5207 Acc: 0.7459\n",
      "has spend time 147m 51s/n\n",
      "val Loss: 0.5496 Acc: 0.7190\n",
      "has spend time 147m 51s/n\n",
      "\n",
      "Epoch 4165/9999\n",
      "----------\n",
      "train Loss: 0.5271 Acc: 0.7213\n",
      "has spend time 147m 53s/n\n",
      "val Loss: 0.5449 Acc: 0.7059\n",
      "has spend time 147m 54s/n\n",
      "\n",
      "Epoch 4166/9999\n",
      "----------\n",
      "train Loss: 0.5200 Acc: 0.7213\n",
      "has spend time 147m 55s/n\n",
      "val Loss: 0.5450 Acc: 0.7124\n",
      "has spend time 147m 56s/n\n",
      "\n",
      "Epoch 4167/9999\n",
      "----------\n",
      "train Loss: 0.5193 Acc: 0.7295\n",
      "has spend time 147m 57s/n\n",
      "val Loss: 0.5474 Acc: 0.7059\n",
      "has spend time 147m 58s/n\n",
      "\n",
      "Epoch 4168/9999\n",
      "----------\n",
      "train Loss: 0.4979 Acc: 0.7336\n",
      "has spend time 147m 60s/n\n",
      "val Loss: 0.5435 Acc: 0.7059\n",
      "has spend time 148m 0s/n\n",
      "\n",
      "Epoch 4169/9999\n",
      "----------\n",
      "train Loss: 0.5245 Acc: 0.7049\n",
      "has spend time 148m 2s/n\n",
      "val Loss: 0.5392 Acc: 0.7255\n",
      "has spend time 148m 3s/n\n",
      "\n",
      "Epoch 4170/9999\n",
      "----------\n",
      "train Loss: 0.5034 Acc: 0.7459\n",
      "has spend time 148m 4s/n\n",
      "val Loss: 0.5470 Acc: 0.7059\n",
      "has spend time 148m 5s/n\n",
      "\n",
      "Epoch 4171/9999\n",
      "----------\n",
      "train Loss: 0.5189 Acc: 0.7418\n",
      "has spend time 148m 6s/n\n",
      "val Loss: 0.5515 Acc: 0.7059\n",
      "has spend time 148m 7s/n\n",
      "\n",
      "Epoch 4172/9999\n",
      "----------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Process Process-34947:\n",
      "Process Process-34948:\n",
      "Process Process-34945:\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/jinbo/anaconda3/lib/python3.5/multiprocessing/process.py\", line 252, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/jinbo/anaconda3/lib/python3.5/multiprocessing/process.py\", line 252, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/jinbo/anaconda3/lib/python3.5/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/jinbo/anaconda3/lib/python3.5/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/jinbo/anaconda3/lib/python3.5/site-packages/torch/utils/data/dataloader.py\", line 106, in _worker_loop\n",
      "    samples = collate_fn([dataset[i] for i in batch_indices])\n",
      "  File \"/home/jinbo/anaconda3/lib/python3.5/site-packages/torch/utils/data/dataloader.py\", line 106, in _worker_loop\n",
      "    samples = collate_fn([dataset[i] for i in batch_indices])\n",
      "  File \"/home/jinbo/anaconda3/lib/python3.5/site-packages/torch/utils/data/dataloader.py\", line 106, in <listcomp>\n",
      "    samples = collate_fn([dataset[i] for i in batch_indices])\n",
      "  File \"/home/jinbo/anaconda3/lib/python3.5/site-packages/torch/utils/data/dataloader.py\", line 106, in <listcomp>\n",
      "    samples = collate_fn([dataset[i] for i in batch_indices])\n",
      "  File \"/home/jinbo/anaconda3/lib/python3.5/site-packages/torchvision/datasets/folder.py\", line 101, in __getitem__\n",
      "    sample = self.loader(path)\n",
      "  File \"/home/jinbo/anaconda3/lib/python3.5/site-packages/torchvision/datasets/folder.py\", line 101, in __getitem__\n",
      "    sample = self.loader(path)\n",
      "  File \"/home/jinbo/anaconda3/lib/python3.5/site-packages/torchvision/datasets/folder.py\", line 147, in default_loader\n",
      "    return pil_loader(path)\n",
      "  File \"/home/jinbo/anaconda3/lib/python3.5/site-packages/torchvision/datasets/folder.py\", line 147, in default_loader\n",
      "    return pil_loader(path)\n",
      "  File \"/home/jinbo/anaconda3/lib/python3.5/site-packages/torchvision/datasets/folder.py\", line 130, in pil_loader\n",
      "    return img.convert('RGB')\n",
      "  File \"/home/jinbo/anaconda3/lib/python3.5/site-packages/torchvision/datasets/folder.py\", line 130, in pil_loader\n",
      "    return img.convert('RGB')\n",
      "  File \"/home/jinbo/anaconda3/lib/python3.5/site-packages/PIL/Image.py\", line 892, in convert\n",
      "    self.load()\n",
      "  File \"/home/jinbo/anaconda3/lib/python3.5/site-packages/PIL/Image.py\", line 892, in convert\n",
      "    self.load()\n",
      "  File \"/home/jinbo/anaconda3/lib/python3.5/site-packages/PIL/ImageFile.py\", line 235, in load\n",
      "    n, err_code = decoder.decode(b)\n",
      "  File \"/home/jinbo/anaconda3/lib/python3.5/site-packages/PIL/ImageFile.py\", line 235, in load\n",
      "    n, err_code = decoder.decode(b)\n",
      "KeyboardInterrupt\n",
      "KeyboardInterrupt\n",
      "Process Process-34946:\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/jinbo/anaconda3/lib/python3.5/multiprocessing/process.py\", line 252, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/jinbo/anaconda3/lib/python3.5/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/jinbo/anaconda3/lib/python3.5/site-packages/torch/utils/data/dataloader.py\", line 106, in _worker_loop\n",
      "    samples = collate_fn([dataset[i] for i in batch_indices])\n",
      "  File \"/home/jinbo/anaconda3/lib/python3.5/multiprocessing/process.py\", line 252, in _bootstrap\n",
      "    self.run()\n",
      "KeyboardInterrupt\n",
      "  File \"/home/jinbo/anaconda3/lib/python3.5/site-packages/torch/utils/data/dataloader.py\", line 106, in <listcomp>\n",
      "    samples = collate_fn([dataset[i] for i in batch_indices])\n",
      "  File \"/home/jinbo/anaconda3/lib/python3.5/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-14-6a0556aaa7e2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m model_ft = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler,\n\u001b[0;32m----> 2\u001b[0;31m                        num_epochs=10000)\n\u001b[0m",
      "\u001b[0;32m<ipython-input-11-12c1784ab149>\u001b[0m in \u001b[0;36mtrain_model\u001b[0;34m(model, criterion, optimizer, scheduler, num_epochs)\u001b[0m\n\u001b[1;32m     41\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     42\u001b[0m                 \u001b[0;31m# statistics\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 43\u001b[0;31m                 \u001b[0mrunning_loss\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     44\u001b[0m                 \u001b[0mrunning_corrects\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpreds\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     45\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  File \"/home/jinbo/anaconda3/lib/python3.5/site-packages/torch/utils/data/dataloader.py\", line 106, in _worker_loop\n",
      "    samples = collate_fn([dataset[i] for i in batch_indices])\n",
      "  File \"/home/jinbo/anaconda3/lib/python3.5/site-packages/torchvision/datasets/folder.py\", line 101, in __getitem__\n",
      "    sample = self.loader(path)\n",
      "  File \"/home/jinbo/anaconda3/lib/python3.5/site-packages/torch/utils/data/dataloader.py\", line 106, in <listcomp>\n",
      "    samples = collate_fn([dataset[i] for i in batch_indices])\n",
      "  File \"/home/jinbo/anaconda3/lib/python3.5/site-packages/torchvision/datasets/folder.py\", line 101, in __getitem__\n",
      "    sample = self.loader(path)\n",
      "  File \"/home/jinbo/anaconda3/lib/python3.5/site-packages/torchvision/datasets/folder.py\", line 147, in default_loader\n",
      "    return pil_loader(path)\n",
      "  File \"/home/jinbo/anaconda3/lib/python3.5/site-packages/torchvision/datasets/folder.py\", line 130, in pil_loader\n",
      "    return img.convert('RGB')\n",
      "  File \"/home/jinbo/anaconda3/lib/python3.5/site-packages/PIL/Image.py\", line 892, in convert\n",
      "    self.load()\n",
      "  File \"/home/jinbo/anaconda3/lib/python3.5/site-packages/torchvision/datasets/folder.py\", line 147, in default_loader\n",
      "    return pil_loader(path)\n",
      "  File \"/home/jinbo/anaconda3/lib/python3.5/site-packages/PIL/ImageFile.py\", line 235, in load\n",
      "    n, err_code = decoder.decode(b)\n",
      "KeyboardInterrupt\n",
      "  File \"/home/jinbo/anaconda3/lib/python3.5/site-packages/torchvision/datasets/folder.py\", line 130, in pil_loader\n",
      "    return img.convert('RGB')\n",
      "  File \"/home/jinbo/anaconda3/lib/python3.5/site-packages/PIL/Image.py\", line 892, in convert\n",
      "    self.load()\n",
      "  File \"/home/jinbo/anaconda3/lib/python3.5/site-packages/PIL/ImageFile.py\", line 235, in load\n",
      "    n, err_code = decoder.decode(b)\n"
     ]
    }
   ],
   "source": [
    "model_ft = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler,\n",
    "                       num_epochs=10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
